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ICIP14(4062-4066)
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Covariance matrices. Search for single query in larger target image.
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Semantics, Visualization, Cats, Rats, Seals, Measurement, Task analysis,
Zero-shot learning, few-shot learning,
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1906
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Chen, Z.C.[Zheng-Chao],
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Automatic Detection of Track and Fields in China from High-Resolution
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Complex, varied structures.
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Area-weighted, context information, one-stage,
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Semantics, Object detection, Task analysis, Visualization, Motorcycles,
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PAMI(46), No. 3, March 2024, pp. 1530-1544.
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Visualization, Semantics, Object detection, Task analysis, Proposals,
Feature extraction, Computational modeling, Object detection,
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Springer DOI
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BibRef
Chen, X.,
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IEEE DOI
2007
Object detection, low-shot learning, continuous learning,
deep learning, transfer learning
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Chen, F.Y.[Fang-Yi],
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Elsevier DOI
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Object detection, Norm calibration, Feature selection
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Zhu, C.C.[Chen-Chen],
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Savvides, M.[Marios],
Feature Selective Anchor-Free Module for Single-Shot Object Detection,
CVPR19(840-849).
IEEE DOI
2002
BibRef
Zhu, C.C.[Chen-Chen],
Chen, F.Y.[Fang-Yi],
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Savvides, M.[Marios],
Soft Anchor-point Object Detection,
ECCV20(IX:91-107).
Springer DOI
2011
Boost performance of anchor-point, with same speed advantage.
BibRef
Li, Y.,
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Cao, J.,
Shen, J.,
Shao, L.,
Improving Single Shot Object Detection With Feature Scale Unmixing,
IP(30), 2021, pp. 2708-2721.
IEEE DOI
2102
Feature extraction, Detectors, Object detection, Visualization,
Semantics, Sports, Real-time systems, Object detection,
feature erasing
BibRef
Pambala, A.K.[Ayyappa Kumar],
Dutta, T.[Titir],
Biswas, S.[Soma],
SML: Semantic meta-learning for few-shot semantic segmentation?,
PRL(147), 2021, pp. 93-99.
Elsevier DOI
2106
Few-shot learning, Semantic segmentation, Attributes
BibRef
Kim, G.[Geonuk],
Jung, H.G.[Hong-Gyu],
Lee, S.W.[Seong-Whan],
Spatial reasoning for few-shot object detection,
PR(120), 2021, pp. 108118.
Elsevier DOI
2109
Few-shot learning, Object detection, Transfer learning,
Visual reasoning, Data augmentation
BibRef
Huang, X.[Xu],
He, B.[Bokun],
Tong, M.[Ming],
Wang, D.W.[Ding-Wen],
He, C.[Chu],
Few-Shot Object Detection on Remote Sensing Images via Shared
Attention Module and Balanced Fine-Tuning Strategy,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Miao, S.Y.[Shu-Yu],
Du, S.S.[Shan-Shan],
Feng, R.[Rui],
Zhang, Y.J.[Yue-Jie],
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Zheng, L.[Lin],
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Balanced single-shot object detection using cross-context
attention-guided network,
PR(122), 2022, pp. 108258.
Elsevier DOI
2112
Cross-context attention-guided network, Cross-context attention mechanism,
Accuracy and speed balance
BibRef
Chen, P.Y.[Ping-Yang],
Chang, M.C.[Ming-Ching],
Hsieh, J.W.[Jun-Wei],
Chen, Y.S.[Yong-Sheng],
Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate
Single-Shot Object Detection,
IP(30), 2021, pp. 9099-9111.
IEEE DOI
2112
Training, Location awareness, Visualization, Purification, Fuses,
Bidirectional control, Object detection, Feature pyramid network,
feature fusion
BibRef
Chen, T.[Tao],
Xie, G.S.[Guo-Sen],
Yao, Y.Z.[Ya-Zhou],
Wang, Q.[Qiong],
Shen, F.M.[Fu-Min],
Tang, Z.M.[Zhen-Min],
Zhang, J.[Jian],
Semantically Meaningful Class Prototype Learning for One-Shot Image
Segmentation,
MultMed(24), 2022, pp. 968-980.
IEEE DOI
2202
Image segmentation, Prototypes, Semantics, Training,
Feature extraction, Task analysis, Testing, Image segmentation,
semantically meaningful prototype
BibRef
Vu, A.K.N.[Anh-Khoa Nguyen],
Nguyen, N.D.[Nhat-Duy],
Nguyen, K.D.[Khanh-Duy],
Nguyen, V.T.[Vinh-Tiep],
Ngo, T.D.[Thanh Duc],
Do, T.T.[Thanh-Toan],
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Few-shot object detection via baby learning,
IVC(120), 2022, pp. 104398.
Elsevier DOI
2204
Few-shot object detection, Few-shot learning, Baby learning
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Cheng, M.[Meng],
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Long, Y.[Yu],
Meta-Learning-Based Incremental Few-Shot Object Detection,
CirSysVideo(32), No. 4, April 2022, pp. 2158-2169.
IEEE DOI
2204
Object detection, Feature extraction, Detectors, Adaptation models,
Training, Task analysis, Data models, Few-shot learning,
object detection
BibRef
Feng, H.T.[Hang-Tao],
Zhang, L.[Lu],
Yang, X.[Xu],
Liu, Z.Y.[Zhi-Yong],
Incremental few-shot object detection via knowledge transfer,
PRL(156), 2022, pp. 67-73.
Elsevier DOI
2205
Machine learning, Convolutional neural networks,
Transfer learning, Incremental few-shot object detection
BibRef
Zhang, X.S.[Xiao-Song],
Wan, F.[Fang],
Liu, C.[Chang],
Ji, X.Y.[Xiang-Yang],
Ye, Q.X.[Qi-Xiang],
Learning to Match Anchors for Visual Object Detection,
PAMI(44), No. 6, June 2022, pp. 3096-3109.
IEEE DOI
2205
Detectors, Location awareness, Feature extraction, Training,
Maximum likelihood estimation, Object detection, Visualization,
generalized linear model
BibRef
Li, B.H.[Bo-Hao],
Yang, B.[Boyu],
Liu, C.[Chang],
Liu, F.[Feng],
Ji, R.R.[Rong-Rong],
Ye, Q.X.[Qi-Xiang],
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object
Detection,
CVPR21(7359-7368)
IEEE DOI
2111
Training, Location awareness, Systematics, Codes,
Object detection, Detectors
BibRef
Wang, H.[Hao],
Wang, Q.L.[Qi-Long],
Zhang, H.Z.[Hong-Zhi],
Hu, Q.H.[Qing-Hua],
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CrabNet: Fully Task-Specific Feature Learning for One-Stage Object
Detection,
IP(31), 2022, pp. 2962-2974.
IEEE DOI
2205
Location awareness, Task analysis, Feature extraction,
Object detection, Representation learning, Detectors, Proposals,
feature interaction
BibRef
Wang, H.[Hao],
Jia, T.[Tong],
Ma, B.[Bowen],
Wang, Q.L.[Qi-Long],
Zuo, W.M.[Wang-Meng],
Fully Cascade Consistency Learning for One-Stage Object Detection,
CirSysVideo(33), No. 10, October 2023, pp. 5986-5998.
IEEE DOI
2310
BibRef
Wang, Y.[Yan],
Xu, C.F.[Chao-Fei],
Liu, C.W.[Cui-Wei],
Li, Z.K.[Zhao-Kui],
Context Information Refinement for Few-Shot Object Detection in
Remote Sensing Images,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Chen, S.Q.[Shi-Qi],
Zhang, J.[Jun],
Zhan, R.H.[Rong-Hui],
Zhu, R.Q.[Rong-Qiang],
Wang, W.[Wei],
Few Shot Object Detection for SAR Images via Feature Enhancement and
Dynamic Relationship Modeling,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Liu, S.[Sixu],
You, Y.[Yanan],
Su, H.Z.[Hao-Zheng],
Meng, G.[Gang],
Yang, W.[Wei],
Liu, F.[Fang],
Few-Shot Object Detection in Remote Sensing Image Interpretation:
Opportunities and Challenges,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Lu, X.K.[Xian-Kai],
Wang, W.G.[Wen-Guan],
Shen, J.B.[Jian-Bing],
Crandall, D.J.[David J.],
Van Gool, L.J.[Luc J.],
Segmenting Objects From Relational Visual Data,
PAMI(44), No. 11, November 2022, pp. 7885-7897.
IEEE DOI
2210
Image segmentation, Visualization, Integrated circuits,
Task analysis, Frequency selective surfaces, Semantics,
few-shot semantic segmentation
BibRef
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Polizzotto, S.[Salvatore],
Caputo, B.[Barbara],
Tommasi, T.[Tatiana],
Self-supervision and meta-learning for one-shot unsupervised
cross-domain detection,
CVIU(223), 2022, pp. 103549.
Elsevier DOI
2210
Cross-domain learning, Object detection, Self-supervision,
Meta-learning, One-shot adaptation
BibRef
d'Innocente, A.[Antonio],
Cappio Borlino, F.[Francesco],
Bucci, S.[Silvia],
Caputo, B.[Barbara],
Tommasi, T.[Tatiana],
One-shot Unsupervised Cross-Domain Detection,
ECCV20(XVI: 732-748).
Springer DOI
2010
BibRef
Liu, W.D.[Wei-De],
Zhang, C.[Chi],
Lin, G.S.[Guo-Sheng],
Liu, F.Y.[Fa-Yao],
CRCNet: Few-Shot Segmentation with Cross-Reference and Region-Global
Conditional Networks,
IJCV(130), No. 12, December 2022, pp. 3140-3157.
Springer DOI
2211
BibRef
Earlier:
CRNet: Cross-Reference Networks for Few-Shot Segmentation,
CVPR20(4164-4172)
IEEE DOI
2008
Image segmentation, Task analysis, Training, Predictive models,
Fuses, Testing, Data models
BibRef
Xu, Q.X.[Qian-Xiong],
Zhao, W.T.[Wen-Ting],
Lin, G.S.[Guo-Sheng],
Long, C.[Cheng],
Self-Calibrated Cross Attention Network for Few-Shot Segmentation,
ICCV23(655-665)
IEEE DOI Code:
WWW Link.
2401
BibRef
Meneghetti, L.[Laura],
Demo, N.[Nicola],
Rozza, G.[Gianluigi],
A Proper Orthogonal Decomposition Approach for Parameters Reduction
of Single Shot Detector Networks,
ICIP22(2206-2210)
IEEE DOI
2211
Training, Image coding, Transfer learning, Object detection,
Robustness, Real-time systems, Image Processing, Object Detection,
Convolutional Neural Network
BibRef
Zeng, T.[Tao],
Xu, F.[Feng],
Lyu, X.[Xin],
Li, X.[Xin],
Wang, X.Y.[Xin-Yuan],
Chen, J.[Jiale],
Wu, C.F.[Cai-Feng],
Feature difference for single-shot object detection,
IET-IPR(16), No. 14, 2022, pp. 3876-3892.
DOI Link
2212
BibRef
Yang, Z.[Ze],
Zhang, C.[Chi],
Li, R.[Ruibo],
Xu, Y.[Yi],
Lin, G.S.[Guo-Sheng],
Efficient Few-Shot Object Detection via Knowledge Inheritance,
IP(32), 2023, pp. 321-334.
IEEE DOI
2301
Detectors, Feature extraction, Benchmark testing, Object detection,
Training, Task analysis, Proposals, Few-shot object detection,
meta learning
BibRef
Sun, B.[Bo],
Li, B.H.[Bang-Huai],
Cai, S.C.[Sheng-Cai],
Yuan, Y.[Ye],
Zhang, C.[Chi],
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding,
CVPR21(7348-7358)
IEEE DOI
2111
Training, Visualization, Pipelines, Object detection,
Benchmark testing, Encoding, Power capacitors
BibRef
Shi, X.W.[Xiang-Wen],
Cui, Z.[Zhe],
Zhang, S.B.[Shao-Bing],
Cheng, M.[Miao],
He, L.[Lian],
Tang, X.[Xianghong],
Multi-similarity based hyperrelation network for few-shot
segmentation,
IET-IPR(17), No. 1, 2023, pp. 204-214.
DOI Link
2301
BibRef
Li, Y.W.[Yue-Wen],
Feng, W.Q.[Wen-Quan],
Lyu, S.C.[Shu-Chang],
Zhao, Q.[Qi],
Feature reconstruction and metric based network for few-shot object
detection,
CVIU(227), 2023, pp. 103600.
Elsevier DOI
2301
Few-shot object detection, Meta-learning, Metric learning,
Feature representation, Pearson distance
BibRef
Zhang, T.Y.[Tian-Yang],
Zhang, X.R.[Xiang-Rong],
Zhu, P.[Peng],
Jia, X.P.[Xiu-Ping],
Tang, X.[Xu],
Jiao, L.C.[Li-Cheng],
Generalized few-shot object detection in remote sensing images,
PandRS(195), 2023, pp. 353-364.
Elsevier DOI
2301
Generalized few-shot object detection, Remote sensing images,
Transfer-learning, Metric learning
BibRef
Li, Y.P.[Yun-Peng],
Zhang, X.R.[Xiang-Rong],
Cheng, X.[Xina],
Tang, X.[Xu],
Jiao, L.C.[Li-Cheng],
Learning consensus-aware semantic knowledge for remote sensing image
captioning,
PR(145), 2024, pp. 109893.
Elsevier DOI
2311
Cross-modal understanding, Visual-semantic interaction,
Remote sensing image captioning, Graph convolutional network
BibRef
Dong, R.C.[Ru-Chan],
Yin, S.Y.[Shun-Yao],
Jiao, L.C.[Li-Cheng],
An, J.[Jungang],
Wu, W.J.[Wen-Jing],
ASIPNet: Orientation-Aware Learning Object Detection for Remote
Sensing Images,
RS(16), No. 16, 2024, pp. 2992.
DOI Link
2408
See also Transferred Deep Learning-Based Change Detection in Remote Sensing Images.
BibRef
Chen, C.F.[Chao-Fan],
Yang, X.S.[Xiao-Shan],
Zhang, J.P.[Jin-Peng],
Dong, B.[Bo],
Xu, C.S.[Chang-Sheng],
Category Knowledge-Guided Parameter Calibration for Few-Shot Object
Detection,
IP(32), 2023, pp. 1092-1107.
IEEE DOI
2302
Detectors, Object detection, Training, Task analysis, Prototypes,
Annotations, Reliability, Object detection, few-shot learning,
graph neural network
BibRef
Huang, G.[Gabriel],
Laradji, I.[Issam],
Vázquez, D.[David],
Lacoste-Julien, S.[Simon],
Rodríguez, P.[Pau],
A Survey of Self-Supervised and Few-Shot Object Detection,
PAMI(45), No. 4, April 2023, pp. 4071-4089.
IEEE DOI
2303
Survey, Few-Shot Object Detection. Object detection, Detectors, Transformers, Feature extraction,
Task analysis, Head, Benchmark testing, Self-supervised, few-shot,
instance segmentation
BibRef
Cheng, G.[Gong],
Lang, C.[Chunbo],
Han, J.W.[Jun-Wei],
Holistic Prototype Activation for Few-Shot Segmentation,
PAMI(45), No. 4, April 2023, pp. 4650-4666.
IEEE DOI
2303
Prototypes, Image segmentation, Task analysis, Semantics,
Feature extraction, Decoding, Training, Few-shot learning, cross-reference
BibRef
Liu, S.[Shifan],
Ma, A.[Ailong],
Pan, S.[Shaoming],
Zhong, Y.F.[Yan-Fei],
An Effective Task Sampling Strategy Based on Category Generation for
Fine-Grained Few-Shot Object Recognition,
RS(15), No. 6, 2023, pp. 1552.
DOI Link
2304
BibRef
Xu, Y.Q.[Yun-Qiu],
Zhou, C.L.[Chun-Luan],
Yu, X.[Xin],
Yang, Y.[Yi],
Cyclic Self-Training With Proposal Weight Modulation for
Cross-Supervised Object Detection,
IP(32), 2023, pp. 1992-2002.
IEEE DOI
2304
Annotations, Proposals, Object detection, Labeling,
Integrated circuits, Training, Detectors, cross supervision
BibRef
Xu, Y.Q.[Yun-Qiu],
Sun, Y.F.[Yi-Fan],
Yang, Z.X.[Zong-Xin],
Miao, J.X.[Jia-Xu],
Yang, Y.[Yi],
H2FA R-CNN: Holistic and Hierarchical Feature Alignment for
Cross-domain Weakly Supervised Object Detection,
CVPR22(14309-14319)
IEEE DOI
2210
Adaptation models, Head, Codes, Annotations, Pipelines,
Object detection, Recognition: detection, categorization,
Self- semi- meta- Transfer/low-shot/long-tail learning
BibRef
Zheng, Z.[Zewen],
Huang, G.[Guoheng],
Yuan, X.C.[Xiao-Chen],
Pun, C.M.[Chi-Man],
Liu, H.R.[Hong-Rui],
Ling, W.K.[Wing-Kuen],
Quaternion-Valued Correlation Learning for Few-Shot Semantic
Segmentation,
CirSysVideo(33), No. 5, May 2023, pp. 2102-2115.
IEEE DOI
2305
Quaternions, Correlation, Convolution, Semantics,
Semantic segmentation, Quantum cascade lasers, Task analysis,
quaternion-valued convolution
BibRef
Sun, H.L.[Hao-Liang],
Lu, X.K.[Xian-Kai],
Wang, H.C.[Hao-Chen],
Yin, Y.L.[Yi-Long],
Zhen, X.T.[Xian-Tong],
Snoek, C.G.M.[Cees G.M.],
Shao, L.[Ling],
Attentional prototype inference for few-shot segmentation,
PR(142), 2023, pp. 109726.
Elsevier DOI
2307
Few-shot segmentation, Variational inference,
Probabilistic model, Latent attention
BibRef
Wang, H.C.[Hao-Chen],
Yang, Y.D.[Yan-Dan],
Cao, X.B.[Xian-Bin],
Zhen, X.T.[Xian-Tong],
Snoek, C.G.M.[Cees G.M.],
Shao, L.[Ling],
Variational Prototype Inference for Few-Shot Semantic Segmentation,
WACV21(525-534)
IEEE DOI
2106
Image segmentation, Uncertainty, Semantics, Prototypes,
Benchmark testing, Probabilistic logic
BibRef
Lu, Z.[Zhihe],
He, S.[Sen],
Li, D.[Da],
Song, Y.Z.[Yi-Zhe],
Xiang, T.[Tao],
Prediction Calibration for Generalized Few-Shot Semantic Segmentation,
IP(32), 2023, pp. 3311-3323.
IEEE DOI
2307
Transformers, Semantic segmentation, Calibration, Training,
Task analysis, Prototypes, Adaptation models,
feature-score cross-covariance transformer
BibRef
Wang, B.[Bin],
Ma, G.R.[Guo-Rui],
Sui, H.G.[Hai-Gang],
Zhang, Y.X.[Yong-Xian],
Zhang, H.M.[Hai-Ming],
Zhou, Y.[Yuan],
Few-Shot Object Detection in Remote Sensing Imagery via Fuse Context
Dependencies and Global Features,
RS(15), No. 14, 2023, pp. 3462.
DOI Link
2307
BibRef
Lang, C.B.[Chun-Bo],
Cheng, G.[Gong],
Tu, B.F.[Bin-Fei],
Li, C.[Chao],
Han, J.W.[Jun-Wei],
Base and Meta: A New Perspective on Few-Shot Segmentation,
PAMI(45), No. 9, September 2023, pp. 10669-10686.
IEEE DOI
2309
BibRef
Zhou, D.W.[Da-Wei],
Ye, H.J.[Han-Jia],
Ma, L.[Liang],
Xie, D.[Di],
Pu, S.L.[Shi-Liang],
Zhan, D.C.[De-Chuan],
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks,
PAMI(45), No. 11, November 2023, pp. 12816-12831.
IEEE DOI
2310
BibRef
Zhou, D.W.[Da-Wei],
Wang, F.Y.[Fu-Yun],
Ye, H.J.[Han-Jia],
Ma, L.[Liang],
Pu, S.L.[Shi-Liang],
Zhan, D.C.[De-Chuan],
Forward Compatible Few-Shot Class-Incremental Learning,
CVPR22(9036-9046)
IEEE DOI
2210
Training, Learning systems, Computational modeling, Prototypes,
Resists, Machine learning, Predictive models,
Transfer/low-shot/long-tail learning
BibRef
Lang, C.[Chunbo],
Cheng, G.[Gong],
Tu, B.[Binfei],
Li, C.[Chao],
Han, J.W.[Jun-Wei],
Retain and Recover: Delving Into Information Loss for Few-Shot
Segmentation,
IP(32), 2023, pp. 5353-5365.
IEEE DOI Code:
WWW Link.
2310
BibRef
Xiong, P.W.[Peng-Wen],
Tong, X.B.[Xiao-Bao],
Liu, P.X.[Peter X.],
Song, A.[Aiguo],
Li, Z.J.[Zhi-Jun],
Robotic Object Perception Based on Multispectral Few-Shot Coupled
Learning,
SMCS(53), No. 10, October 2023, pp. 6119-6131.
IEEE DOI
2310
BibRef
Chen, Z.H.[Zhi-Hao],
Mao, Y.C.[Ying-Chi],
Qian, Y.[Yong],
Pan, Z.X.[Zhen-Xiang],
Xu, S.F.[Shu-Fang],
FRDet: Few-shot object detection via feature reconstruction,
IET-IPR(17), No. 12, 2023, pp. 3599-3615.
DOI Link
2310
object detection, few-shot learning, metric learning, feature reconstruction
BibRef
Huang, J.[Junying],
Cao, J.H.[Jun-Hao],
Lin, L.[Liang],
Zhang, D.[Dongyu],
IRA-FSOD: Instant-Response and Accurate Few-Shot Object Detector,
CirSysVideo(33), No. 11, November 2023, pp. 6912-6923.
IEEE DOI
2311
BibRef
Yang, Z.Y.[Zhen-Yu],
Zhang, Y.X.[Yong-Xin],
Zheng, J.[Jv],
Yu, Z.B.[Zhi-Bin],
Zheng, B.[Bing],
Scale Information Enhancement for Few-Shot Object Detection on Remote
Sensing Images,
RS(15), No. 22, 2023, pp. 5372.
DOI Link
2311
BibRef
Zhao, X.W.[Xiao-Wei],
Liu, X.L.[Xiang-Long],
Ma, Y.Q.[Yu-Qing],
Bai, S.H.[Shi-Hao],
Shen, Y.F.[Yi-Fan],
Hao, Z.[Zeyu],
Liu, A.[Aishan],
Temporal Speciation Network for Few-Shot Object Detection,
MultMed(25), 2023, pp. 8267-8278.
IEEE DOI
2312
BibRef
Vu, A.K.N.[Anh-Khoa Nguyen],
Do, T.T.[Thanh-Toan],
Nguyen, N.D.[Nhat-Duy],
Nguyen, V.T.[Vinh-Tiep],
Ngo, T.D.[Thanh Duc],
Nguyen, T.V.[Tam V.],
Instance-Level Few-Shot Learning With Class Hierarchy Mining,
IP(32), 2023, pp. 2374-2385.
IEEE DOI
2305
Feature extraction, Training, Data mining, Training data,
Task analysis, Proposals, Object detection, Few-shot learning,
hierarchical information
BibRef
Xu, J.[Jinbo],
Wang, Y.[Yong],
He, X.Y.[Xiao-Yu],
Zou, Y.Q.[Yi-Qun],
Support-Query Mutual Promotion and Classification Correction Network
for Few-Shot Object Detection,
SPLetters(31), 2024, pp. 201-205.
IEEE DOI
2401
BibRef
Zhai, W.[Wei],
Wu, P.Y.[Ping-Yu],
Zhu, K.[Kai],
Cao, Y.[Yang],
Wu, F.[Feng],
Zha, Z.J.[Zheng-Jun],
Background Activation Suppression for Weakly Supervised Object
Localization and Semantic Segmentation,
IJCV(132), No. 3, March 2024, pp. 750-775.
Springer DOI
2402
BibRef
Earlier: A2, A1, A4, Only:
Background Activation Suppression for Weakly Supervised Object
Localization,
CVPR22(14228-14237)
IEEE DOI
2210
Location awareness, Correlation, Codes, Generators,
Task analysis, Recognition: detection,
Self- semi- meta- unsupervised learning
BibRef
Meng, M.[Meng],
Zhang, T.Z.[Tian-Zhu],
Tian, Q.[Qi],
Zhang, Y.D.[Yong-Dong],
Wu, F.[Feng],
Foreground Activation Maps for Weakly Supervised Object Localization,
ICCV21(3365-3375)
IEEE DOI
2203
Location awareness, Scalability, Computational modeling,
Benchmark testing, Task analysis, Standards,
Recognition and classification
BibRef
Huang, Q.[Qihan],
Zhang, H.F.[Hao-Fei],
Xue, M.Q.[Meng-Qi],
Song, J.[Jie],
Song, M.L.[Ming-Li],
A Survey of Deep Learning for Low-shot Object Detection,
Surveys(56), No. 5, November 2023, pp. xx-yy.
DOI Link
2402
meta-learning, transfer-learning, zero-shot object detection,
one-shot object detection, Few-shot object detection
BibRef
Ding, J.[Jun],
Zhang, Z.[Zhen],
Wang, Q.Y.[Qi-Yu],
Wang, H.B.[Hui-Bin],
SCTrans: Self-align and cross-align transformer for few-shot
segmentation,
IVC(142), 2024, pp. 104893.
Elsevier DOI
2402
Semantic segmentation, Few-shot learning, Few-shot segmentation
BibRef
Lang, C.B.[Chun-Bo],
Cheng, G.[Gong],
Tu, B.F.[Bin-Fei],
Han, J.W.[Jun-Wei],
Few-Shot Segmentation via Divide-and-Conquer Proxies,
IJCV(132), No. 1, January 2024, pp. 261-283.
Springer DOI
2402
BibRef
Chen, Y.[Yadang],
Chen, S.[Sihan],
Yang, Z.X.[Zhi-Xin],
Wu, E.[Enhua],
Learning self-target knowledge for few-shot segmentation,
PR(149), 2024, pp. 110266.
Elsevier DOI
2403
Few-shot segmentation, Two-level similarity matching,
Step-by-step mining, Attention mechanism
BibRef
Chen, Y.[Yadang],
Xu, X.Y.[Xin-Yu],
Wei, C.C.[Chen-Chen],
Lu, C.H.[Chu-Han],
Prototype-wise self-knowledge distillation for few-shot segmentation,
SP:IC(129), 2024, pp. 117186.
Elsevier DOI
2411
Few-shot segmentation, Data augmentation, Self-knowledge distillation
BibRef
Lu, Y.[Yue],
Chen, X.Y.[Xing-Yu],
Wu, Z.X.[Zheng-Xing],
Tan, M.[Min],
Yu, J.Z.[Jun-Zhi],
Binary Similarity Few-Shot Object Detection With Modeling of Hard
Negative Samples,
MultMed(26), 2024, pp. 4805-4818.
IEEE DOI
2403
Head, Finite element analysis, Object detection, Detectors,
Feature extraction, Training, Proposals, Few-shot learning,
deep learning
BibRef
Wang, M.[Meng],
Wang, Y.[Yang],
Liu, H.P.[Hai-Peng],
Explicit knowledge transfer of graph-based correlation distillation
and diversity data hallucination for few-shot object detection,
IVC(143), 2024, pp. 104958.
Elsevier DOI
2403
Few-shot object detection, Graph convolutional network,
Knowledge distillation, Data hallucination
BibRef
Jung, M.J.[Min Jae],
Han, S.D.[Seung Dae],
Kim, J.[Joohee],
Re-scoring using image-language similarity for few-shot object
detection,
CVIU(241), 2024, pp. 103956.
Elsevier DOI Code:
WWW Link.
2403
Few-shot object detection, Few-shot Learning, Object detection,
Vision-language multi modal, Classification loss
BibRef
Wang, Y.[Yun],
Zhu, L.[Lu],
Liu, Y.Y.[Yuan-Yuan],
CFENet: Boosting Few-Shot Semantic Segmentation With Complementary
Feature-Enhanced Network,
MultMed(26), 2024, pp. 5630-5640.
IEEE DOI
2404
Feature extraction, Prototypes, Semantic segmentation, Data mining,
Task analysis, Correlation, Transformers, Few-shot learning,
few-shot semantic segmentation
BibRef
Tang, Y.[Yingbo],
Cao, Z.Q.[Zhi-Qiang],
Yang, Y.[Yuequan],
Liu, J.[Jierui],
Yu, J.Z.[Jun-Zhi],
Semi-Supervised Few-Shot Object Detection via Adaptive Pseudo
Labeling,
CirSysVideo(34), No. 4, April 2024, pp. 2151-2165.
IEEE DOI
2404
Adaptation models, Object detection, Data models, Training,
Detectors, Labeling, Feature extraction, Few-shot object detection,
semi-supervised learning
BibRef
Chen, S.[Shuai],
Meng, F.M.[Fan-Man],
Zhang, R.T.[Run-Tong],
Qiu, H.Q.[He-Qian],
Li, H.L.[Hong-Liang],
Wu, Q.B.[Qing-Bo],
Xu, L.F.[Lin-Feng],
Visual and Textual Prior Guided Mask Assemble for Few-Shot
Segmentation and Beyond,
MultMed(26), 2024, pp. 7197-7209.
IEEE DOI
2405
Task analysis, Visualization, Image segmentation, Annotations,
Prototypes, Adaptation models, Training, Few-shot segmentation, CLIP
BibRef
Li, S.[Shiyue],
Yang, G.[Guan],
Liu, X.M.[Xiao-Ming],
Huang, K.[Kekun],
Liu, Y.[Yang],
Few-shot object detection based on global context and implicit
knowledge decoupled head,
IET-IPR(18), No. 6, 2024, pp. 1460-1474.
DOI Link
2405
convolutional neural nets, object detection
BibRef
Cao, Q.L.[Qing-Long],
Chen, Y.[Yuntian],
Ma, C.[Chao],
Yang, X.K.[Xiao-Kang],
Break the Bias: Delving Semantic Transform Invariance for Few-Shot
Segmentation,
CirSysVideo(34), No. 5, May 2024, pp. 3971-3982.
IEEE DOI Code:
WWW Link.
2405
Transforms, Semantics, Semantic segmentation, Prototypes,
Task analysis, Feature extraction, Training, Multi-view matching,
few-shot segmentation
BibRef
Chang, Z.B.[Zhao-Bin],
Gao, X.[Xiong],
Li, N.[Na],
Zhou, H.Y.[Hui-Yu],
Lu, Y.G.[Yong-Gang],
DRNet: Disentanglement and Recombination Network for Few-Shot
Semantic Segmentation,
CirSysVideo(34), No. 7, July 2024, pp. 5560-5574.
IEEE DOI Code:
WWW Link.
2407
Semantic segmentation, Prototypes, Task analysis,
Transformers, Semantics, Correlation, joint learning
BibRef
Yan, B.[Bowei],
Lang, C.[Chunbo],
Cheng, G.[Gong],
Han, J.W.[Jun-Wei],
Understanding Negative Proposals in Generic Few-Shot Object Detection,
CirSysVideo(34), No. 7, July 2024, pp. 5818-5829.
IEEE DOI Code:
WWW Link.
2407
Training, Annotations, Object detection, Proposals, Detectors,
Metalearning, Deep learning, sampling algorithm
BibRef
Wu, J.S.[Jia-Shan],
Lang, C.[Chunbo],
Cheng, G.[Gong],
Xie, X.X.[Xing-Xing],
Han, J.W.[Jun-Wei],
Retentive Compensation and Personality Filtering for Few-Shot Remote
Sensing Object Detection,
CirSysVideo(34), No. 7, July 2024, pp. 5805-5817.
IEEE DOI Code:
WWW Link.
2407
Remote sensing, Prototypes, Object detection, Task analysis,
Filtering, Training, Satellite images, Few-shot object detection,
metric learning
BibRef
Chen, H.[Han],
Wang, Q.[Qi],
Xie, K.L.[Kai-Lin],
Lei, L.[Liang],
Lin, M.G.[Matthieu Gaetan],
Lv, T.[Tian],
Liu, Y.J.[Yong-Jin],
Luo, J.B.[Jie-Bo],
SD-FSOD: Self-Distillation Paradigm via Distribution Calibration for
Few-Shot Object Detection,
CirSysVideo(34), No. 7, July 2024, pp. 5963-5976.
IEEE DOI
2407
Prototypes, Task analysis, Object detection, Feature extraction,
Calibration, Predictive models, Detectors,
decoupled sub-tasks
BibRef
Yang, H.Q.[Han-Qing],
Cai, S.[Sijia],
Deng, B.[Bing],
Ye, J.P.[Jie-Ping],
Lin, G.S.[Guo-Sheng],
Zhang, Y.[Yu],
Context-Aware and Semantic-Consistent Spatial Interactions for
One-Shot Object Detection Without Fine-Tuning,
CirSysVideo(34), No. 7, July 2024, pp. 5424-5439.
IEEE DOI
2407
Correlation, Feature extraction, Transformers, Object detection, Tensors,
Semantics, Detectors, Object detection, one-shot, spatial interaction
BibRef
Yang, L.X.[Ling-Xiao],
Chen, D.P.[Da-Peng],
Chen, Y.F.[Yi-Fei],
Peng, W.[Wei],
Xie, X.H.[Xiao-Hua],
A Neuroinspired Contrast Mechanism enables Few-Shot Object Detection,
PR(156), 2024, pp. 110766.
Elsevier DOI
2408
Few-Shot Object Detection, Contrast Blocks, Faster R-CNN, Perceptual Learning
BibRef
Zhu, J.X.[Jin-Xiang],
Wang, Q.[Qi],
Dong, X.Y.[Xin-Yu],
Ruan, W.J.[Wei-Jian],
Chen, H.L.[Hao-Lin],
Lei, L.[Liang],
Hao, G.[Gefei],
FSNA: Few-Shot Object Detection via Neighborhood Information Adaption
and All Attention,
CirSysVideo(34), No. 8, August 2024, pp. 7121-7134.
IEEE DOI
2408
Object detection, Task analysis, Metalearning, Data models, Tuning,
Training, Target recognition, Few-shot learning, object detection,
attention mechanism
BibRef
Fan, Q.[Qi],
Zhuo, W.[Wei],
Tang, C.K.[Chi-Keung],
Tai, Y.W.[Yu-Wing],
FSODv2: A Deep Calibrated Few-Shot Object Detection Network,
IJCV(132), No. 1, January 2024, pp. 3566-3585.
Springer DOI
2409
BibRef
He, H.[Haolan],
Dong, X.G.[Xian-Guo],
Zhou, X.F.[Xiao-Fei],
Wang, B.[Bo],
Zhang, J.Y.[Ji-Yong],
Interactive Fusion and Correlation Network for Three-Modal Images
Few-Shot Semantic Segmentation,
SPLetters(31), 2024, pp. 2430-2434.
IEEE DOI
2410
Correlation, Fuses, Decoding, Feature extraction, Convolution,
Visualization, Water resources, Few-shot learning, semantic segmentation
BibRef
Duan, L.J.[Li-Juan],
Liu, G.Y.[Guang-Yuan],
En, Q.[Qing],
Liu, Z.Y.[Zhao-Ying],
Gong, Z.[Zhi],
Ma, B.[Bian],
Enhancing zero-shot object detection with external knowledge-guided
robust contrast learning,
PRL(185), 2024, pp. 152-159.
Elsevier DOI
2410
Zero-shot object detection, External knowledge, Supervised contrastive learning
BibRef
Huang, P.L.[Pei-Liang],
Zhang, D.W.[Ding-Wen],
Cheng, D.[De],
Han, L.F.[Long-Fei],
Zhu, P.F.[Peng-Fei],
Han, J.W.[Jun-Wei],
M-RRFS: A Memory-Based Robust Region Feature Synthesizer for Zero-Shot
Object Detection,
IJCV(132), No. 10, October 2024, pp. 4651-4672.
Springer DOI
2410
BibRef
Earlier: A1, A6, A3, A2, Only:
Robust Region Feature Synthesizer for Zero-Shot Object Detection,
CVPR22(7612-7621)
IEEE DOI
2210
Visualization, Synthesizers, Semantics, Object detection, Detectors,
Feature extraction, Recognition: detection, categorization,
Transfer/low-shot/long-tail learning
BibRef
Jiang, Z.Y.[Zhi-Yu],
Yuan, Y.[Ye],
Yuan, Y.[Yuan],
Prototypical Metric Segment Anything Model for Data-Free Few-Shot
Semantic Segmentation,
SPLetters(31), 2024, pp. 2800-2804.
IEEE DOI
2410
Prototypes, Training, Semantic segmentation,
Autonomous aerial vehicles, Measurement, Semantics, Annotations,
segment anything model (SAM)
BibRef
Xin, Z.M.[Zhi-Meng],
Wu, T.X.[Tian-Xu],
Chen, S.M.[Shi-Ming],
Zou, Y.X.[Yi-Xiong],
Shao, L.[Ling],
You, X.G.[Xin-Ge],
ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection,
IP(33), 2024, pp. 5564-5576.
IEEE DOI Code:
WWW Link.
2410
Training, Detectors, Object detection, Feature extraction,
Task analysis, Semantics, Adaptation models, co-existing regions
BibRef
Li, W.[Weikai],
Wei, H.F.[Hong-Feng],
Wu, Y.[Yanlai],
Yang, J.[Jie],
Ruan, Y.[Yudi],
Li, Y.[Yuan],
Tang, Y.[Ying],
TIDE: Test-Time Few-Shot Object Detection,
SMCS(54), No. 11, November 2024, pp. 6500-6509.
IEEE DOI Code:
WWW Link.
2410
Tides, Feature extraction, Adaptation models, Task analysis,
Training, Object detection, Visualization, Cross attention,
test time
BibRef
Zhang, J.[Jing],
Hong, Z.[Zhaolong],
Chen, X.[Xu],
Li, Y.S.[Yun-Song],
Few-Shot Object Detection for Remote Sensing Imagery Using
Segmentation Assistance and Triplet Head,
RS(16), No. 19, 2024, pp. 3630.
DOI Link
2410
BibRef
Yin, A.[Ating],
Wang, Y.[Yaonan],
Mao, J.[Jianxu],
Zhang, H.[Hui],
Chen, X.[Xiuyi],
Category-Contextual Relation Encoding Network for Few-Shot Object
Detection,
CirSysVideo(34), No. 9, September 2024, pp. 8355-8367.
IEEE DOI
2410
Proposals, Encoding, Object detection, Detectors, Task analysis,
Semantics, Few-shot learning,
inter-class relation encoding
BibRef
Wang, Z.[Zheng],
Gao, Y.J.[Ying-Jie],
Liu, Q.J.[Qing-Jie],
Wang, Y.H.[Yun-Hong],
Semantic Enhanced Few-Shot Object Detection,
ICIP24(575-581)
IEEE DOI
2411
Degradation, Visualization, Semantics, Object detection, Proposals,
Information exchange, Few-shot object detection,
margin loss
BibRef
Wang, Y.J.[Yan-Jie],
Zou, X.[Xu],
Yan, L.X.[Lu-Xin],
Zhong, S.[Sheng],
Zhou, J.H.[Jia-Huan],
SNIDA: Unlocking Few-Shot Object Detection with Non-Linear Semantic
Decoupling Augmentation,
CVPR24(12544-12553)
IEEE DOI
2410
Fuses, Semantic segmentation, Computational modeling, Semantics,
Training data, Object detection, object detection,
data augmentation
BibRef
Bou, X.[Xavier],
Facciolo, G.[Gabriele],
von Gioi, R.G.[Rafael Grompone],
Morel, J.M.[Jean-Michel],
Ehret, T.[Thibaud],
Exploring Robust Features for Few-Shot Object Detection in Satellite
Imagery,
EarthVision24(430-439)
IEEE DOI
2410
Training, Visualization, Vocabulary,
Single instruction multiple data, Prototypes, Object detection, Detectors
BibRef
Wang, J.[Jin],
Zhang, B.F.[Bing-Feng],
Pang, J.[Jian],
Chen, H.L.[Hong-Long],
Liu, W.F.[Wei-Feng],
Rethinking Prior Information Generation with CLIP for Few-Shot
Segmentation,
CVPR24(3941-3951)
IEEE DOI Code:
WWW Link.
2410
Training, Location awareness, Visualization, Image segmentation,
Accuracy, Semantics, Feature extraction, semantic segmentation,
few-shot learning
BibRef
Hossain, M.R.I.[Mir Rayat Imtiaz],
Siam, M.[Mennatullah],
Sigal, L.[Leonid],
Little, J.J.[James J.],
Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale
Approach,
CVPR24(23470-23480)
IEEE DOI
2410
Visualization, Attention mechanisms, Semantic segmentation,
Benchmark testing, Transformers,
transductive fine-tuning
BibRef
Fahes, M.[Mohammad],
Vu, T.H.[Tuan-Hung],
Bursuc, A.[Andrei],
Pérez, P.[Patrick],
de Charette, R.[Raoul],
A Simple Recipe for Language-Guided Domain Generalized Segmentation,
CVPR24(23428-23437)
IEEE DOI Code:
WWW Link.
2410
Training, Codes, Semantic segmentation, Computational modeling,
Neural networks, Benchmark testing
BibRef
Zhao, D.[Dong],
Wang, S.[Shuang],
Zang, Q.[Qi],
Jiao, L.C.[Li-Cheng],
Sebe, N.[Nicu],
Zhong, Z.[Zhun],
Stable Neighbor Denoising for Source-free Domain Adaptive
Segmentation,
CVPR24(23416-23427)
IEEE DOI Code:
WWW Link.
2410
Semantic segmentation, Source coding, Noise reduction, Noise,
Benchmark testing, Data models
BibRef
He, W.Z.[Wei-Zhao],
Zhang, Y.[Yang],
Zhuo, W.[Wei],
Shen, L.L.[Lin-Lin],
Yang, J.Q.[Jia-Qi],
Deng, S.[Songhe],
Sun, L.[Liang],
APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic
Segmentation,
CVPR24(23762-23772)
IEEE DOI
2410
Training, Metalearning, Visualization, Semantic segmentation,
Semantics, Prototypes, Training data
BibRef
Tong, J.T.[Jin-Tao],
Zhou, H.C.[Hai-Chen],
Liu, Y.C.[Yi-Cong],
Hu, Y.M.[Yi-Man],
Zou, Y.X.[Yi-Xiong],
Dynamic Knowledge Adapter with Probabilistic Calibration for
Generalized Few-Shot Semantic Segmentation,
L3D-IVU24(2781-2790)
IEEE DOI
2410
Training, Knowledge engineering, Adaptation models, Semantic segmentation,
Predictive models, Probabilistic logic, Stability analysis
BibRef
Zhou, Z.Q.[Zi-Qin],
Xu, H.M.[Hai-Ming],
Shu, Y.Y.[Yang-Yang],
Liu, L.Q.[Ling-Qiao],
Unlocking the Potential of Pre-Trained Vision Transformers for
Few-Shot Semantic Segmentation through Relationship Descriptors,
CVPR24(3817-3827)
IEEE DOI Code:
WWW Link.
2410
Training, Adaptation models, Visualization, Codes,
Semantic segmentation, Computational modeling
BibRef
Buettner, K.[Kyle],
Kovashka, A.[Adriana],
Investigating the Role of Attribute Context in Vision-Language Models
for Object Recognition and Detection,
WACV24(5462-5472)
IEEE DOI
2404
Training, Analytical models, Sensitivity, Grounding,
Computational modeling, Object detection, Algorithms
BibRef
Liu, Z.M.[Zhuo-Ming],
Hu, X.F.[Xue-Feng],
Nevatia, R.[Ram],
Efficient Feature Distillation for Zero-shot Annotation Object
Detection,
WACV24(882-891)
IEEE DOI Code:
WWW Link.
2404
Training, Schedules, Annotations, Semantics, Training data,
Object detection, Detectors, Algorithms,
Vision + language and/or other modalities
BibRef
Chen, H.[Hao],
Dong, Y.[Yonghan],
Lu, Z.[Zheming],
Yu, Y.L.[Yun-Long],
Han, J.G.[Jun-Gong],
Pixel Matching Network for Cross-Domain Few-Shot Segmentation,
WACV24(967-976)
IEEE DOI Code:
WWW Link.
2404
Training, Image segmentation, Matched filters, Filtering,
Interference, Benchmark testing, Algorithms
BibRef
Moon, S.[Seonghyeon],
Sohn, S.S.[Samuel S.],
Zhou, H.[Honglu],
Yoon, S.[Sejong],
Pavlovic, V.[Vladimir],
Khan, M.H.[Muhammad Haris],
Kapadia, M.[Mubbasir],
MSI: Maximize Support-Set Information for Few-Shot Segmentation,
ICCV23(19209-19219)
IEEE DOI Code:
WWW Link.
2401
BibRef
Liu, J.[Jie],
Du, Y.J.[Ying-Jun],
Xiao, Z.[Zehao],
Snoek, C.G.M.[Cees G.M],
Sonke, J.J.[Jan-Jakob],
Gavves, E.[Efstratios],
Memory-augmented Variational Adaptation for Online Few-shot
Segmentation,
VCL23(3316-3325)
IEEE DOI
2401
BibRef
Shangguan, Z.[Zeyu],
Rostami, M.[Mohammad],
Identification of Novel Classes for Improving Few-Shot Object
Detection,
VCL23(3348-3358)
IEEE DOI Code:
WWW Link.
2401
BibRef
Du, J.H.[Jin-Hao],
Zhang, S.[Shan],
Chen, Q.[Qiang],
Le, H.F.[Hai-Feng],
Sun, Y.P.[Yan-Peng],
Ni, Y.[Yao],
Wang, J.[Jian],
He, B.[Bin],
Wang, J.D.[Jing-Dong],
sigma-Adaptive Decoupled Prototype for Few-Shot Object Detection,
ICCV23(18904-18914)
IEEE DOI
2401
BibRef
Yu, Z.M.[Zhi-Miao],
Lin, T.C.[Tian-Cheng],
Xu, Y.[Yi],
Background Clustering Pre-Training for Few-Shot Segmentation,
ICIP23(1695-1699)
IEEE DOI Code:
WWW Link.
2312
BibRef
Peng, B.[Bohao],
Tian, Z.[Zhuotao],
Wu, X.Y.[Xiao-Yang],
Wang, C.Y.[Cheng-Yao],
Liu, S.[Shu],
Su, J.Y.[Jing-Yong],
Jia, J.Y.[Jia-Ya],
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation,
CVPR23(23641-23651)
IEEE DOI
2309
BibRef
Ma, J.W.[Jia-Wei],
Niu, Y.[Yulei],
Xu, J.C.[Jin-Cheng],
Huang, S.Y.[Shi-Yuan],
Han, G.X.[Guang-Xing],
Chang, S.F.[Shih-Fu],
DiGeo: Discriminative Geometry-Aware Learning for Generalized
Few-Shot Object Detection,
CVPR23(3208-3218)
IEEE DOI
2309
BibRef
Xu, J.Y.[Jing-Yi],
Le, H.[Hieu],
Samaras, D.[Dimitris],
Generating Features with Increased Crop-Related Diversity for
Few-Shot Object Detection,
CVPR23(19713-19722)
IEEE DOI
2309
BibRef
Lin, S.B.[Shao-Bo],
Wang, K.[Kun],
Zeng, X.Y.[Xing-Yu],
Zhao, R.[Rui],
Explore the Power of Synthetic Data on Few-shot Object Detection,
GCV23(638-647)
IEEE DOI
2309
BibRef
Lin, S.B.[Shao-Bo],
Wang, K.[Kun],
Zeng, X.Y.[Xing-Yu],
Zhao, R.[Rui],
An Effective Crop-Paste Pipeline for Few-shot Object Detection,
L3D-IVU23(4820-4828)
IEEE DOI
2309
BibRef
Demirel, B.[Berkan],
Baran, O.B.[Orhun Bugra],
Cinbis, R.G.[Ramazan Gokberk],
Meta-Tuning Loss Functions and Data Augmentation for Few-Shot Object
Detection,
CVPR23(7339-7349)
IEEE DOI
2309
BibRef
Guirguis, K.[Karim],
Meier, J.[Johannes],
Eskandar, G.[George],
Kayser, M.[Matthias],
Yang, B.[Bin],
Beyerer, J.[Jürgen],
NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection
via Neural Instance Feature Forging,
CVPR23(24193-24202)
IEEE DOI
2309
BibRef
Guirguis, K.[Karim],
Eskandar, G.[George],
Wang, M.Y.[Ming-Yang],
Kayser, M.[Matthias],
Monari, E.[Eduardo],
Yang, B.[Bin],
Beyerer, J.[Jürgen],
Uncertainty-based Forgetting Mitigation for Generalized Few-Shot
Object Detection,
L3D-IVU24(2586-2595)
IEEE DOI
2410
Training, Uncertainty, Service robots, Prevention and mitigation,
Estimation, Object detection, Predictive models, Object Detection, G-FSOD
BibRef
Guirguis, K.[Karim],
Hendawy, A.[Ahmed],
Eskandar, G.[George],
Abdelsamad, M.[Mohamed],
Kayser, M.[Matthias],
Beyerer, J.[Jürgen],
CFA: Constraint-based Finetuning Approach for Generalized Few-Shot
Object Detection,
L3D-IVU22(4038-4048)
IEEE DOI
2210
Learning systems, Adaptation models, Object detection,
Search problems
BibRef
Wang, Y.[Yuan],
Sun, R.[Rui],
Zhang, T.Z.[Tian-Zhu],
Rethinking the Correlation in Few-Shot Segmentation: A Buoys View,
CVPR23(7183-7192)
IEEE DOI
2309
BibRef
Wang, Y.[Yuan],
Sun, R.[Rui],
Zhang, Z.[Zhe],
Zhang, T.Z.[Tian-Zhu],
Adaptive Agent Transformer for Few-Shot Segmentation,
ECCV22(XXIX:36-52).
Springer DOI
2211
BibRef
Lin, S.B.[Shao-Bo],
Zeng, X.Y.[Xing-Yu],
Yan, S.L.[Shi-Lin],
Zhao, R.[Rui],
Three-stage Training Pipeline with Patch Random Drop for Few-shot
Object Detection,
ACCV22(VI:286-302).
Springer DOI
2307
BibRef
Fan, Q.[Qi],
Tang, C.K.[Chi-Keung],
Tai, Y.W.[Yu-Wing],
Few-Shot Object Detection with Model Calibration,
ECCV22(XIX:720-739).
Springer DOI
2211
BibRef
Fan, Q.[Qi],
Zhuo, W.,
Tang, C.K.[Chi-Keung],
Tai, Y.W.[Yu-Wing],
Few-Shot Object Detection With Attention-RPN and Multi-Relation
Detector,
CVPR20(4012-4021)
IEEE DOI
2008
Object detection, Training, Task analysis, Detectors, Proposals,
Semantics
BibRef
Huang, K.[Kai],
Cheng, M.F.[Ming-Fei],
Wang, Y.[Yang],
Wang, B.C.[Bo-Chen],
Xi, Y.[Ye],
Wang, F.[Feigege],
Chen, P.[Peng],
A Joint Framework Towards Class-aware and Class-Agnostic Alignment for
Few-Shot Segmentation,
ACCV22(VII:431-447).
Springer DOI
2307
BibRef
Chen, S.[Song],
Wang, C.[Chong],
Liu, W.J.[Wei-Jie],
Ye, Z.J.[Zheng-Jie],
Deng, J.C.[Jia-Cheng],
Pseudo-label Diversity Exploitation for Few-shot Object Detection,
MMMod23(II: 289-300).
Springer DOI
2304
BibRef
Jiang, X.Y.[Xin-Yu],
Li, Z.J.[Zheng-Jia],
Tian, M.Q.[Mao-Qing],
Liu, J.B.[Jian-Bo],
Yi, S.[Shuai],
Miao, D.Q.[Duo-Qian],
Few-shot Object Detection via Improved Classification Features,
WACV23(5375-5384)
IEEE DOI
2302
Adaptation models, Computational modeling, Object detection,
Benchmark testing, Feature extraction, visual reasoning
BibRef
Guirguis, K.[Karim],
Abdelsamad, M.[Mohamed],
Eskandar, G.[George],
Hendawy, A.[Ahmed],
Kayser, M.[Matthias],
Yang, B.[Bin],
Beyerer, J.[Juergen],
Towards Discriminative and Transferable One-Stage Few-Shot Object
Detectors,
WACV23(3749-3758)
IEEE DOI
2302
Training, Annotations, Object detection, Detectors,
Benchmark testing, Data models, visual reasoning
BibRef
Kayabasi, A.[Alper],
Tüfekci, G.[Gülin],
Ulusoy, I.[Ilkay],
Elimination of Non-Novel Segments at Multi-Scale for Few-Shot
Segmentation,
WACV23(2558-2566)
IEEE DOI
2302
Training, Image segmentation, Computational modeling,
Predictive models, Benchmark testing, Ensemble learning,
Biomedical/healthcare/medicine
BibRef
Matcovici, S.[Stefan],
Voinea, D.[Daniel],
Popa, A.I.[Alin-Ionut],
k-NN embeded space conditioning for enhanced few-shot object
detection,
Novelty23(401-410)
IEEE DOI
2302
Training, Visualization, Conferences, Object detection,
Predictive models, Benchmark testing
BibRef
Kim, S.[Sueyeon],
Nam, W.J.[Woo-Jeoung],
Lee, S.W.[Seong-Whan],
Few-Shot Object Detection with Proposal Balance Refinement,
ICPR22(4700-4707)
IEEE DOI
2212
Object detection, Detectors, Feature extraction,
Proposals, few-shot learning, object detection
BibRef
Guan, H.Y.[Hao-Yan],
Michael, S.[Spratling],
CobNet: Cross Attention on Object and Background for Few-Shot
Segmentation,
ICPR22(39-45)
IEEE DOI
2212
Image segmentation, Annotations, Benchmark testing,
Feature extraction, Data mining, Object recognition, Standards
BibRef
Li, B.[Bowen],
Wang, C.[Chen],
Reddy, P.[Pranay],
Kim, S.[Seungchan],
Scherer, S.[Sebastian],
AirDet: Few-Shot Detection Without Fine-Tuning for Autonomous
Exploration,
ECCV22(XXIX:427-444).
Springer DOI
2211
BibRef
Johnander, J.[Joakim],
Edstedt, J.[Johan],
Felsberg, M.[Michael],
Khan, F.S.[Fahad Shahbaz],
Danelljan, M.[Martin],
Dense Gaussian Processes for Few-Shot Segmentation,
ECCV22(XXIX:217-234).
Springer DOI
2211
BibRef
Pathiraja, B.[Bimsara],
Gunawardhana, M.[Malitha],
Khan, M.H.[Muhammad Haris],
Multiclass Confidence and Localization Calibration for Object
Detection,
CVPR23(19734-19743)
IEEE DOI
2309
BibRef
Moon, S.[Seonghyeon],
Sohn, S.S.[Samuel S.],
Zhou, H.[Honglu],
Yoon, S.[Sejong],
Pavlovic, V.[Vladimir],
Khan, M.H.[Muhammad Haris],
Kapadia, M.[Mubbasir],
HM: Hybrid Masking for Few-Shot Segmentation,
ECCV22(XX:506-523).
Springer DOI
2211
BibRef
Zhang, S.[Shan],
Murray, N.[Naila],
Wang, L.[Lei],
Koniusz, P.[Piotr],
Time-rEversed DiffusioN tEnsor Transformer: A New TENET of Few-Shot
Object Detection,
ECCV22(XX:310-328).
Springer DOI
2211
BibRef
Earlier: A1, A3, A2, A4:
Kernelized Few-shot Object Detection with Efficient Integral
Aggregation,
CVPR22(19185-19194)
IEEE DOI
2210
Image coding, Costs, Pipelines, Object detection, Detectors,
Feature extraction, Representation learning, retrieval
BibRef
Zhang, S.[Shan],
Luo, D.W.[Da-Wei],
Wang, L.[Lei],
Koniusz, P.[Piotr],
Few-shot Object Detection by Second-order Pooling,
ACCV20(IV:369-387).
Springer DOI
2103
BibRef
Lee, K.[Kibok],
Yang, H.[Hao],
Chakraborty, S.[Satyaki],
Cai, Z.W.[Zhao-Wei],
Swaminathan, G.[Gurumurthy],
Ravichandran, A.[Avinash],
Dabeer, O.[Onkar],
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark,
ECCV22(XX:366-382).
Springer DOI
2211
BibRef
Ma, T.X.[Tian-Xue],
Bi, M.W.[Ming-Wei],
Zhang, J.[Jian],
Yuan, W.[Wang],
Zhang, Z.Z.[Zhi-Zhong],
Xie, Y.[Yuan],
Ding, S.H.[Shou-Hong],
Ma, L.Z.[Li-Zhuang],
Mutually Reinforcing Structure with Proposal Contrastive Consistency
for Few-Shot Object Detection,
ECCV22(XX:400-416).
Springer DOI
2211
BibRef
Gao, Y.P.[Yi-Peng],
Yang, L.X.[Ling-Xiao],
Huang, Y.[Yunmu],
Xie, S.[Song],
Li, S.Y.[Shi-Yong],
Zheng, W.S.[Wei-Shi],
AcroFOD: An Adaptive Method for Cross-Domain Few-Shot Object Detection,
ECCV22(XXXIII:673-690).
Springer DOI
2211
BibRef
Yoo, J.[Jayeon],
Chung, I.[Inseop],
Kwak, N.[Nojun],
Unsupervised Domain Adaptation for One-Stage Object Detector Using
Offsets to Bounding Box,
ECCV22(XXXIII:691-708).
Springer DOI
2211
BibRef
Fan, Q.[Qi],
Tang, C.K.[Chi-Keung],
Tai, Y.W.[Yu-Wing],
Few-Shot Video Object Detection,
ECCV22(XX:76-98).
Springer DOI
2211
BibRef
Elezi, I.[Ismail],
Yu, Z.[Zhiding],
Anandkumar, A.[Anima],
Leal-Taixé, L.[Laura],
Alvarez, J.M.[Jose M.],
Not All Labels Are Equal:
Rationalizing The Labeling Costs for Training Object Detection,
CVPR22(14472-14481)
IEEE DOI
2210
Training, Costs, Uncertainty, Neural networks, Object detection,
Detectors, Robustness, Self- semi- meta- Emergency Reviews
BibRef
Li, H.J.[Han-Jun],
Pan, X.J.[Xing-Jia],
Yan, K.[Ke],
Tang, F.[Fan],
Zheng, W.S.[Wei-Shi],
SIOD: Single Instance Annotated Per Category Per Image for Object
Detection,
CVPR22(14177-14186)
IEEE DOI
2210
Location awareness, Costs, Annotations, Object detection, Solids,
Reliability, Recognition: detection, categorization, retrieval,
Self- semi- meta- unsupervised learning
BibRef
Ma, J.W.[Jia-Wei],
Han, G.X.[Guang-Xing],
Huang, S.Y.[Shi-Yuan],
Yang, Y.C.[Yun-Cong],
Chang, S.F.[Shih-Fu],
Few-Shot End-to-End Object Detection via Constantly Concentrated
Encoding Across Heads,
ECCV22(XXVI:57-73).
Springer DOI
2211
BibRef
Han, G.X.[Guang-Xing],
Ma, J.W.[Jia-Wei],
Huang, S.Y.[Shi-Yuan],
Chen, L.[Long],
Chang, S.F.[Shih-Fu],
Few-Shot Object Detection with Fully Cross-Transformer,
CVPR22(5311-5320)
IEEE DOI
2210
Training, Visualization, Head, Aggregates, Object detection,
Benchmark testing, Feature extraction, Recognition: detection,
Transfer/low-shot/long-tail learning
BibRef
Kaul, P.[Prannay],
Xie, W.[Weidi],
Zisserman, A.[Andrew],
Label, Verify, Correct: A Simple Few Shot Object Detection Method,
CVPR22(14217-14227)
IEEE DOI
2210
Training, Detectors, Object detection, Predictive models,
Benchmark testing, Solids, Recognition: detection, categorization,
Self- semi- meta- Transfer/low-shot/long-tail learning
BibRef
Yang, H.Q.[Han-Qing],
Cai, S.[Sijia],
Sheng, H.[Hualian],
Deng, B.[Bing],
Huang, J.Q.[Jian-Qiang],
Hua, X.S.[Xian-Sheng],
Tang, Y.[Yong],
Zhang, Y.[Yu],
Balanced and Hierarchical Relation Learning for One-shot Object
Detection,
CVPR22(7581-7590)
IEEE DOI
2210
Training, Deep learning, Computational modeling, Semantics,
Detectors, Object detection, Boosting, Recognition: detection,
Transfer/low-shot/long-tail learning
BibRef
Yin, L.[Li],
Perez-Rua, J.M.[Juan M],
Liang, K.J.[Kevin J],
Sylph: A Hypernetwork Framework for Incremental Few-shot Object
Detection,
CVPR22(9025-9035)
IEEE DOI
2210
Training, Location awareness, Deep learning, Fuses, Object detection,
Detectors, retrieval, categorization,
Recognition: detection
BibRef
Hersche, M.[Michael],
Karunaratne, G.[Geethan],
Cherubini, G.[Giovanni],
Benini, L.[Luca],
Sebastian, A.[Abu],
Rahimi, A.[Abbas],
Constrained Few-shot Class-incremental Learning,
CVPR22(9047-9057)
IEEE DOI
2210
Training, Representation learning, Costs, Computational modeling,
Memory management, Interference,
Representation learning
BibRef
Tang, Y.M.[Yu-Ming],
Peng, Y.X.[Yi-Xing],
Zheng, W.S.[Wei-Shi],
Learning to Imagine:
Diversify Memory for Incremental Learning using Unlabeled Data,
CVPR22(9539-9548)
IEEE DOI
2210
Training, Deep learning, Costs, Computational modeling, Semantics,
Neural networks, Recognition: detection, categorization, retrieval
BibRef
Wu, T.Y.[Tz-Ying],
Swaminathan, G.[Gurumurthy],
Li, Z.Z.[Zhi-Zhong],
Ravichandran, A.[Avinash],
Vasconcelos, N.M.[Nuno M.],
Bhotika, R.[Rahul],
Soatto, S.[Stefano],
Class-Incremental Learning with Strong Pre-trained Models,
CVPR22(9591-9600)
IEEE DOI
2210
Training, Adaptation models, Computational modeling, Cloning,
Recognition: detection, categorization, retrieval
BibRef
Dong, J.H.[Jia-Hua],
Wang, L.[Lixu],
Fang, Z.[Zhen],
Sun, G.[Gan],
Xu, S.C.[Shi-Chao],
Wang, X.[Xiao],
Zhu, Q.[Qi],
Federated Class-Incremental Learning,
CVPR22(10154-10163)
IEEE DOI
2210
Training, Privacy, Federated learning, Computational modeling,
Prototypes, Benchmark testing, Propagation losses,
Transfer/low-shot/long-tail learning
BibRef
Liu, H.[Huan],
Gu, L.[Li],
Chi, Z.X.[Zhi-Xiang],
Wang, Y.[Yang],
Yu, Y.H.[Yuan-Hao],
Chen, J.[Jun],
Tang, J.[Jin],
Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free
Replay,
ECCV22(XXIV:146-162).
Springer DOI
2211
BibRef
Chi, Z.X.[Zhi-Xiang],
Gu, L.[Li],
Liu, H.[Huan],
Wang, Y.[Yang],
Yu, Y.H.[Yuan-Hao],
Tang, J.[Jin],
MetaFSCIL:
A Meta-Learning Approach for Few-Shot Class Incremental Learning,
CVPR22(14146-14155)
IEEE DOI
2210
Training, Adaptation models, Protocols, Modulation,
Bidirectional control, Power capacitors,
Recognition: detection
BibRef
Xie, J.W.[Jiang-Wei],
Yan, S.P.[Shi-Peng],
He, X.M.[Xu-Ming],
General Incremental Learning with Domain-aware Categorical
Representations,
CVPR22(14331-14340)
IEEE DOI
2210
Learning systems, Mixture models, Benchmark testing,
Complexity theory, Faces, retrieval,
Recognition: detection
BibRef
Kang, M.S.[Min-Soo],
Park, J.[Jaeyoo],
Han, B.H.[Bo-Hyung],
Class-Incremental Learning by Knowledge Distillation with Adaptive
Feature Consolidation,
CVPR22(16050-16059)
IEEE DOI
2210
Representation learning, Deep learning, Upper bound,
Neural networks, Linear programming, Robustness,
Representation learning
BibRef
Toldo, M.[Marco],
Ozay, M.[Mete],
Bring Evanescent Representations to Life in Lifelong Class
Incremental Learning,
CVPR22(16711-16720)
IEEE DOI
2210
Training, Representation learning, Deep learning,
Analytical models, Computational modeling, Semantics, Data models,
Transfer/low-shot/long-tail learning
BibRef
Villa, A.[Andrés],
Alhamoud, K.[Kumail],
Escorcia, V.[Victor],
Heilbron, F.C.[Fabian Caba],
Alcázar, J.L.[Juan León],
Ghanem, B.[Bernard],
vCLIMB: A Novel Video Class Incremental Learning Benchmark,
CVPR22(19013-19022)
IEEE DOI
2210
Learning systems, Analytical models, Codes, Training data,
Benchmark testing,
Datasets and evaluation
BibRef
Madhu, P.[Prathmesh],
Meyer, A.[Anna],
Zinnen, M.[Mathias],
Mührenberg, L.[Lara],
Suckow, D.[Dirk],
Bendschus, T.[Torsten],
Reinhardt, C.[Corinna],
Bell, P.[Peter],
Verstegen, U.[Ute],
Kosti, R.[Ronak],
Maier, A.[Andreas],
Christlein, V.[Vincent],
One-Shot Object Detection in Heterogeneous Artwork Datasets,
IPTA22(1-6)
IEEE DOI
2206
Training, Adaptation models, Visualization, Archeology, Art, Semantics,
Object detection, one-shot, object detection, digital humanities,
data augmentation
BibRef
Kobayashi, D.[Daisuke],
Self-supervised Prototype Conditional Few-Shot Object Detection,
CIAP22(II:681-692).
Springer DOI
2205
BibRef
Bailer, W.[Werner],
Making Few-Shot Object Detection Simpler and Less Frustrating,
MMMod22(II:445-451).
Springer DOI
2203
BibRef
Wu, A.[Aming],
Han, Y.[Yahong],
Zhu, L.C.[Lin-Chao],
Yang, Y.[Yi],
Universal-Prototype Enhancing for Few-Shot Object Detection,
ICCV21(9547-9556)
IEEE DOI
2203
Representation learning, Visualization, Prototypes,
Object detection, Feature extraction,
Detection and localization in 2D and 3D
BibRef
Han, G.X.[Guang-Xing],
He, Y.C.[Yi-Cheng],
Huang, S.Y.[Shi-Yuan],
Ma, J.W.[Jia-Wei],
Chang, S.F.[Shih-Fu],
Query Adaptive Few-Shot Object Detection with Heterogeneous Graph
Convolutional Networks,
ICCV21(3243-3252)
IEEE DOI
2203
Measurement, Adaptation models, Computational modeling,
Message passing, Image edge detection, Prototypes,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Min, J.H.[Ju-Hong],
Kang, D.[Dahyun],
Cho, M.[Minsu],
Hypercorrelation Squeeze for Few-Shot Segmenation,
ICCV21(6921-6932)
IEEE DOI
2203
Visualization, Image segmentation, Correlation, Tensors, Semantics,
Benchmark testing, Feature extraction, Segmentation,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Qiao, L.M.[Li-Meng],
Zhao, Y.X.[Yu-Xuan],
Li, Z.Y.[Zhi-Yuan],
Qiu, X.[Xi],
Wu, J.A.[Jian-An],
Zhang, C.[Chi],
DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection,
ICCV21(8661-8670)
IEEE DOI
2203
Location awareness, Visualization, Object detection, Detectors,
Benchmark testing, Multitasking, Feature extraction,
BibRef
Yang, S.[Shu],
Zhang, L.[Lu],
Qi, J.Q.[Jin-Qing],
Lu, H.C.[Hu-Chuan],
Wang, S.[Shuo],
Zhang, X.X.[Xiao-Xing],
Learning Motion-Appearance Co-Attention for Zero-Shot Video Object
Segmentation,
ICCV21(1544-1553)
IEEE DOI
2203
Training, Codes, Fuses, Collaboration, Object segmentation,
Interference, Video analysis and understanding,
BibRef
Shaban, A.[Amirreza],
Rahimi, A.[Amir],
Ajanthan, T.[Thalaiyasingam],
Boots, B.[Byron],
Hartley, R.I.[Richard I.],
Few-shot Weakly-Supervised Object Detection via Directional
Statistics,
WACV22(1040-1049)
IEEE DOI
2202
Location awareness, Training, Semantics, Prototypes,
Object detection, Gaussian distribution, Transfer, Few-shot,
Semi- and Un- supervised Learning Object Detection/Recognition/Categorization
BibRef
Lee, H.[Hojun],
Lee, M.G.[Myung-Gi],
Kwak, N.[Nojun],
Few-Shot Object Detection by Attending to Per-Sample-Prototype,
WACV22(1101-1110)
IEEE DOI
2202
Support vector machines, Codes, Prototypes,
Object detection, Benchmark testing, Feature extraction, Transfer,
Semi- and Un- supervised Learning Object Detection/Recognition/Categorization
BibRef
Lee, Y.H.[Yuan-Hao],
Yang, F.E.[Fu-En],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic
Segmentation,
WACV22(1607-1617)
IEEE DOI
2202
Training, Image segmentation,
Computational modeling, Semantics, Benchmark testing,
Semi- and Un- supervised Learning
BibRef
Amac, M.S.[Mustafa Sercan],
Sencan, A.[Ahmet],
Baran, O.B.[Orhun Bugra],
Ikizler-Cinbis, N.[Nazli],
Cinbis, R.G.[Ramazan Gokberk],
MaskSplit: Self-supervised Meta-learning for Few-shot Semantic
Segmentation,
WACV22(428-438)
IEEE DOI
2202
Training, Image segmentation, Adaptation models, Semantics,
Training data, Prototypes, Estimation, Transfer, Few-shot,
Grouping and Shape
BibRef
Zhong, C.L.[Chao-Liang],
Wang, J.[Jie],
Feng, C.[Cheng],
Zhang, Y.[Ying],
Sun, J.[Jun],
Yokota, Y.[Yasuto],
PICA: Point-Wise Instance and Centroid Alignment Based Few-Shot
Domain Adaptive Object Detection with Loose Annotations,
WACV22(398-407)
IEEE DOI
2202
Training, Adaptation models, Annotations,
Computational modeling, Object detection, Predictive models,
Object Detection/Recognition/Categorization
BibRef
Tsironis, V.,
Stentoumis, C.,
Lekkas, N.,
Nikopoulos, A.,
Scale-awareness for More Accurate Object Detection Using Modified
Single Shot Detectors,
ISPRS21(B2-2021: 801-808).
DOI Link
2201
BibRef
Chu, J.H.[Jing-Hui],
Feng, J.W.[Jia-Wei],
Jing, P.G.[Pei-Guang],
Lu, W.[Wei],
Joint Co-Attention and Co-Reconstruction Representation Learning for
One-Shot Object Detection,
ICIP21(2229-2233)
IEEE DOI
2201
Training, Degradation, Correlation, Object detection,
Feature extraction, Proposals, Object detection, one-shot learning,
low-rank co-reconstruction
BibRef
Zheng, Y.[Ye],
Cui, L.[Li],
Zero-Shot Object Detection With Transformers,
ICIP21(444-448)
IEEE DOI
2201
Deep learning, Head, Image processing, Object detection,
Benchmark testing, Natural language processing,
Zero-Shot Learning
BibRef
Luo, X.L.[Xiao-Liu],
Zhang, T.P.[Tai-Ping],
Graph Affinity Network for Few-Shot Segmentation,
ICIP21(609-613)
IEEE DOI
2201
Image segmentation, Annotations, Semantics,
graph convolutional network, graph affinity, few-shot segmentation
BibRef
Wang, Y.[Yu],
Zhang, Y.[Ye],
Zhai, S.H.[Shao-Hua],
Chen, H.[Hao],
Shi, S.Q.[Shao-Qi],
Wang, G.[Gang],
Deep Sensor Fusion Based on Frustum Point Single Shot Multibox
Detector for 3D Object Detection,
ICIP21(674-678)
IEEE DOI
2201
Location awareness, Degradation, Image segmentation, Semantics,
Detectors, Semantic segmentation, frustum point cloud,
object detection
BibRef
Erabati, G.K.[Gopi Krishna],
Araujo, H.[Helder],
SL3D: Single Look 3D Object Detection based on RGB-D Images,
DICTA20(1-8)
IEEE DOI
2201
Fuses, Shape, Object detection, Feature extraction,
Real-time systems, Sun, Object detection, RGB-D, CNN
BibRef
Wolf, S.[Stefan],
Meier, J.[Jonas],
Sommer, L.[Lars],
Beyerer, J.[Jürgen],
Double Head Predictor based Few-Shot Object Detection for Aerial
Imagery,
LUAI21(721-731)
IEEE DOI
2112
WWW Link.
Code, Object Detection. Training, Head, Codes, Annotations, Training data
BibRef
Fan, Z.B.[Zhi-Bo],
Ma, Y.[Yuchen],
Li, Z.[Zeming],
Sun, J.[Jian],
Generalized Few-Shot Object Detection without Forgetting,
CVPR21(4525-4534)
IEEE DOI
2111
Measurement, Transfer learning, Object detection,
Detectors, Benchmark testing, Reliability engineering
BibRef
Li, A.[Aoxue],
Li, Z.G.[Zhen-Guo],
Transformation Invariant Few-Shot Object Detection,
CVPR21(3093-3101)
IEEE DOI
2111
Object detection, Detectors, Predictive models,
Boosting, Data models
BibRef
Zhu, C.C.[Chen-Chen],
Chen, F.[Fangyi],
Ahmed, U.[Uzair],
Shen, Z.Q.[Zhi-Qiang],
Savvides, M.[Marios],
Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection,
CVPR21(8778-8787)
IEEE DOI
2111
Visualization, Protocols, Semantics,
Object detection, Detectors, Image representation
BibRef
Zhang, L.[Lu],
Zhou, S.[Shuigeng],
Guan, J.H.[Ji-Hong],
Zhang, J.[Ji],
Accurate Few-shot Object Detection with Support-Query Mutual Guidance
and Hybrid Loss,
CVPR21(14419-14427)
IEEE DOI
2111
Measurement, Training, Training data,
Object detection, Detectors
BibRef
Li, Y.T.[Yi-Ting],
Zhu, H.Y.[Hai-Yue],
Cheng, Y.[Yu],
Wang, W.X.[Wen-Xin],
Teo, C.S.[Chek Sing],
Xiang, C.[Cheng],
Vadakkepat, P.[Prahlad],
Lee, T.H.[Tong Heng],
Few-Shot Object Detection via Classification Refinement and
Distractor Retreatment,
CVPR21(15390-15398)
IEEE DOI
2111
Training, Degradation, Filtering, Detectors,
Object detection, Boosting
BibRef
Khandelwal, S.[Siddhesh],
Goyal, R.[Raghav],
Sigal, L.[Leonid],
UniT: Unified Knowledge Transfer for Any-shot Object Detection and
Segmentation,
CVPR21(5947-5957)
IEEE DOI
2111
Training, Visualization, Image segmentation,
Computational modeling, Taxonomy, Refining
BibRef
Zhang, W.[Weilin],
Wang, Y.X.[Yu-Xiong],
Hallucination Improves Few-Shot Object Detection,
CVPR21(13003-13012)
IEEE DOI
2111
Training, Computational modeling, Training data,
Object detection, Detectors, Benchmark testing
BibRef
Chen, D.J.[Ding-Jie],
Hsieh, H.Y.[He-Yen],
Liu, T.L.[Tyng-Luh],
Adaptive Image Transformer for One-Shot Object Detection,
CVPR21(12242-12251)
IEEE DOI
2111
Adaptation models, Training data, Object detection,
Predictive models, Feature extraction, Transformers
BibRef
Ying, X.W.[Xiao-Wen],
Li, X.[Xin],
Chuah, M.C.[Mooi Choo],
Weakly-Supervised Object Representation Learning for Few-Shot
Semantic Segmentation,
WACV21(1496-1505)
IEEE DOI
2106
Training, Image segmentation, Annotations,
Semantics, Benchmark testing
BibRef
Cheng, Y.[Yuan],
Yang, Y.C.[Yu-Chao],
Chen, H.B.[Hai-Bao],
Wong, N.[Ngai],
Yu, H.[Hao],
S3-Net: A Fast and Lightweight Video Scene Understanding Network by
Single-shot Segmentation,
WACV21(3328-3336)
IEEE DOI
2106
Quantization (signal), Computational modeling, Semantics,
Graphics processing units, Streaming media, Feature extraction,
Rendering (computer graphics)
BibRef
Agarwal, S.[Shivang],
Jurie, F.[Frederic],
Hierarchical Head Design for Object Detectors,
ICPR21(4981-4988)
IEEE DOI
2105
Training, Head, Detectors, Object detection, Performance gain,
Feature extraction, 2D Object Detection,
Deep Learning
BibRef
Orfanidis, G.[Georgios],
Ioannidis, K.[Konstantinos],
Vrochidis, S.[Stefanos],
Tefas, A.[Anastasios],
Kompatsiaris, I.[Ioannis],
A modified Single-Shot multibox Detector for beyond Real-Time Object
Detection,
ICPR21(3977-3984)
IEEE DOI
2105
Detectors, Object detection, Real-time systems, Timing
BibRef
Zheng, Y.[Ye],
Huang, R.[Ruoran],
Han, C.Q.[Chuan-Qi],
Huang, X.[Xi],
Cui, L.[Li],
Background Learnable Cascade for Zero-shot Object Detection,
ACCV20(III:107-123).
Springer DOI
2103
BibRef
Hayat, N.[Nasir],
Hayat, M.[Munawar],
Rahman, S.[Shafin],
Khan, S.[Salman],
Zamir, S.W.[Syed Waqas],
Khan, F.S.[Fahad Shahbaz],
Synthesizing the Unseen for Zero-shot Object Detection,
ACCV20(III:155-170).
Springer DOI
2103
BibRef
Osokin, A.[Anton],
Sumin, D.[Denis],
Lomakin, V.[Vasily],
Os2d: One-stage One-shot Object Detection by Matching Anchor Features,
ECCV20(XV:635-652).
Springer DOI
2011
detecting objects defined by a single demonstration.
BibRef
Wu, J.X.[Jia-Xi],
Liu, S.T.[Song-Tao],
Huang, D.[Di],
Wang, Y.H.[Yun-Hong],
Multi-scale Positive Sample Refinement for Few-shot Object Detection,
ECCV20(XVI: 456-472).
Springer DOI
2010
BibRef
Jang, H.,
Woo, S.,
Benz, P.,
Park, J.,
Kweon, I.S.,
Propose-and-Attend Single Shot Detector,
WACV20(804-813)
IEEE DOI
2006
Detectors, Training, Convolution, Proposals, Feature extraction,
Standards, Computational modeling
BibRef
Raza, H.,
Ravanbakhsh, M.,
Klein, T.,
Nabi, M.,
Weakly Supervised One Shot Segmentation,
MDALC19(1401-1406)
IEEE DOI
2004
image representation, image segmentation,
learning (artificial intelligence), one-shot learning,
semantic segmentation
BibRef
Siam, M.,
Oreshkin, B.,
Jagersand, M.,
AMP: Adaptive Masked Proxies for Few-Shot Segmentation,
ICCV19(5248-5257)
IEEE DOI
2004
Code, Segmentation.
WWW Link. image fusion, image motion analysis, image segmentation,
learning (artificial intelligence), AMP, adaptive masked proxies,
Feature extraction
BibRef
Yang, Y.W.[Yu-Wei],
Meng, F.M.[Fan-Man],
Li, H.L.[Hong-Liang],
Wu, Q.B.[Qing-Bo],
Xu, X.L.[Xiao-Long],
Chen, S.[Shuai],
A New Local Transformation Module for Few-shot Segmentation,
MMMod20(II:76-87).
Springer DOI
2003
BibRef
Pérez-Rúa, J.,
Zhu, X.,
Hospedales, T.M.,
Xiang, T.,
Incremental Few-Shot Object Detection,
CVPR20(13843-13852)
IEEE DOI
2008
Object detection, Training, Feature extraction, Detectors,
Heating systems, Generators, Robots
BibRef
Wang, S.,
Cao, S.,
Wei, D.,
Wang, R.,
Ma, K.,
Wang, L.,
Meng, D.,
Zheng, Y.,
LT-Net: Label Transfer by Learning Reversible Voxel-Wise
Correspondence for One-Shot Medical Image Segmentation,
CVPR20(9159-9168)
IEEE DOI
2008
Image segmentation, Machine learning,
Medical diagnostic imaging, Training
BibRef
Kang, B.,
Liu, Z.,
Wang, X.,
Yu, F.,
Feng, J.,
Darrell, T.J.,
Few-Shot Object Detection via Feature Reweighting,
ICCV19(8419-8428)
IEEE DOI
2004
convolutional neural nets, feature extraction,
learning (artificial intelligence), object detection,
Training data
BibRef
Chen, S.,
Wang, X.,
Single-Shot Detector with Multiple Inference Paths,
ICIP19(2005-2009)
IEEE DOI
1910
Object detection, resource-constrained, deep networks
BibRef
Li, W.,
Liu, G.,
A Single-Shot Object Detector with Feature Aggregation and
Enhancement,
ICIP19(3910-3914)
IEEE DOI
1910
Real-Time object detection, feature enhancement, feature aggregation
BibRef
Li, S.[Shuai],
Yang, L.X.[Ling-Xiao],
Huang, J.Q.[Jian-Qiang],
Hua, X.S.[Xian-Sheng],
Zhang, L.[Lei],
Dynamic Anchor Feature Selection for Single-Shot Object Detection,
ICCV19(6608-6617)
IEEE DOI
2004
feature extraction, feature selection, image fusion,
object detection, regression analysis
BibRef
He, L.Q.[Li-Qiang],
Todorovic, S.[Sinisa],
DESTR: Object Detection with Split Transformer,
CVPR22(9367-9376)
IEEE DOI
2210
Visualization, Privacy, Object detection, Detectors,
Performance gain, Transformers, Decoding, Recognition: detection, retrieval
BibRef
Nguyen, K.[Khoi],
Todorovic, S.[Sinisa],
Feature Weighting and Boosting for Few-Shot Segmentation,
ICCV19(622-631)
IEEE DOI
2004
foreground objects in images.
convolutional neural nets, feature extraction,
image classification, image segmentation, inference mechanisms,
Computer architecture
BibRef
Qiao, S.Y.[Si-Yuan],
Chen, L.C.[Liang-Chieh],
Yuille, A.L.[Alan L.],
DetectoRS: Detecting Objects with Recursive Feature Pyramid and
Switchable Atrous Convolution,
CVPR21(10208-10219)
IEEE DOI
2111
Philosophical considerations, Codes, Convolution,
Detectors, Switches, Object detection
BibRef
Zhang, Z.,
Qiao, S.,
Xie, C.,
Shen, W.,
Wang, B.,
Yuille, A.L.,
Single-Shot Object Detection with Enriched Semantics,
CVPR18(5813-5821)
IEEE DOI
1812
Semantics, Feature extraction, Object detection,
Image segmentation, Detectors, Task analysis, Visualization
BibRef
Xu, P.,
Zhao, X.,
Huang, K.,
Densely Connected Single-Shot Detector,
ICPR18(2178-2183)
IEEE DOI
1812
Feature extraction, Detectors, Object detection, Convolution,
Transforms, Task analysis
BibRef
Rahman, S.[Shafin],
Khan, S.[Salman],
Barnes, N.,
Deep0Tag: Deep Multiple Instance Learning for Zero-Shot Image Tagging,
MultMed(22), No. 1, January 2020, pp. 242-255.
IEEE DOI
2001
BibRef
Earlier: A1, A2, Only:
Deep Multiple Instance Learning for Zero-Shot Image Tagging,
ACCV18(I:530-546).
Springer DOI
1906
Deep learning, Multiple instance learning, Feature pooling,
Object detection, Zero-shot tagging
BibRef
Xiang, W.,
Zhang, D.Q.,
Yu, H.,
Athitsos, V.,
Context-Aware Single-Shot Detector,
WACV18(1784-1793)
IEEE DOI
1806
SSD object detector.
convolution, object detection, ubiquitous computing, CSSD, SSD, VGGNet,
context layers,
Radio frequency
BibRef
Woo, S.[Sanghyun],
Hwang, S.[Soonmin],
Kweon, I.S.[In So],
StairNet:
Top-Down Semantic Aggregation for Accurate One Shot Detection,
WACV18(1093-1102)
IEEE DOI
1806
feature extraction, image classification, image representation,
object detection, PASCAL VOC 2012 datasets, SSD framework,
Visualization
Compare to SSD and YOLO.
BibRef
Hu, H.,
Lan, S.,
Jiang, Y.,
Cao, Z.,
Sha, F.,
FastMask: Segment Multi-scale Object Candidates in One Shot,
CVPR17(2280-2288)
IEEE DOI
1711
Feature extraction, Head, Image segmentation, Neck, Proposals, Semantics
BibRef
Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
YOLO, You Only Look Once, Family Object Detection .