7.1.7.6 One-Shot Object Detection, Single Shot Detector, and Segmentation

Chapter Contents (Back)
Object Detction. One-Shot Detection. Few-Shot Detection. Single Shot Detection. One Shot.
See also Semi-Supervised Object Detection.
See also Instance of Particular Object, Specified Object.
See also Dense Object Detection.

Pagès, J.[Jordi], Salvi, J.[Joaquim], Collewet, C.[Christophe], Forest, J.[Josep],
Optimised De Bruijn patterns for one-shot shape acquisition,
IVC(23), No. 8, 1 August 2005, pp. 707-720.
Elsevier DOI 0508
BibRef

Lampert, C.H.[Christoph H.], Nickisch, H.[Hannes], Harmeling, S.[Stefan],
Attribute-Based Classification for Zero-Shot Visual Object Categorization,
PAMI(36), No. 3, March 2014, pp. 453-465.
IEEE DOI 1403
BibRef
Earlier:
Learning to detect unseen object classes by between-class attribute transfer,
CVPR09(951-958).
IEEE DOI 0906
computer vision BibRef

Biswas, S.K.[Sujoy Kumar], Milanfar, P.[Peyman],
One Shot Detection with Laplacian Object and Fast Matrix Cosine Similarity,
PAMI(38), No. 3, March 2016, pp. 546-562.
IEEE DOI 1602
BibRef
Earlier:
Laplacian object: One-shot object detection by locality preserving projection,
ICIP14(4062-4066)
IEEE DOI 1502
Covariance matrices. Search for single query in larger target image. BibRef

Chen, S.Q.[Shi-Qi], Zhan, R.H.[Rong-Hui], Zhang, J.[Jun],
Geospatial Object Detection in Remote Sensing Imagery Based on Multiscale Single-Shot Detector with Activated Semantics,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Chen, Z.C.[Zheng-Chao], Lu, K.X.[Kai-Xuan], Gao, L.R.[Lian-Ru], Li, B.P.[Bai-Peng], Gao, J.W.[Jian-Wei], Yang, X.[Xuan], Yao, M.F.[Mu-Feng], Zhang, B.[Bing],
Automatic Detection of Track and Fields in China from High-Resolution Satellite Images Using Multi-Scale-Fused Single Shot MultiBox Detector,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
Complex, varied structures. BibRef

Li, X.Q.[Xiao-Qiang], Liu, C.W.[Chuan-Wei], Dai, S.M.[Song-Min], Lian, H.C.[Hui-Chen], Ding, G.T.[Guang-Tai],
Scale specified single shot multibox detector,
IET-CV(14), No. 2, March 2020, pp. 59-64.
DOI Link 2002
Detection at multiple scales. BibRef

Wang, P.J.[Pei-Jin], Sun, X.[Xian], Diao, W.H.[Wen-Hui], Fu, K.[Kun],
FMSSD: Feature-Merged Single-Shot Detection for Multiscale Objects in Large-Scale Remote Sensing Imagery,
GeoRS(58), No. 5, May 2020, pp. 3377-3390.
IEEE DOI 2005
Area-weighted, context information, one-stage, remote sensing imagery, small object detection BibRef

Yan, C.X.[Cai-Xia], Zheng, Q.H.[Qing-Hua], Chang, X.J.[Xiao-Jun], Luo, M.N.[Min-Nan], Yeh, C.H.[Chung-Hsing], Hauptman, A.G.[Alexander G.],
Semantics-Preserving Graph Propagation for Zero-Shot Object Detection,
IP(29), 2020, pp. 8163-8176.
IEEE DOI 2008
Semantics, Object detection, Task analysis, Visualization, Motorcycles, Bicycles, Correlation, Zero-shot object detection, graph propagation BibRef

Rahman, S.[Shafin], Khan, S.H.[Salman H.], Porikli, F.M.[Fatih M.],
Zero-Shot Object Detection: Joint Recognition and Localization of Novel Concepts,
IJCV(128), No. 12, December 2020, pp. 2979-2999.
Springer DOI 2010
BibRef

Chen, F.Y.[Fang-Yi], Zhu, C.C.[Chen-Chen], Shen, Z.Q.[Zhi-Qiang], Zhang, H.[Han], Savvides, M.[Marios],
NCMS: Towards accurate anchor free object detection through L2 norm calibration and multi-feature selection,
CVIU(200), 2020, pp. 103050.
Elsevier DOI 2010
Object detection, Norm calibration, Feature selection BibRef

Zhu, C.C.[Chen-Chen], He, Y.H.[Yi-Hui], 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], Shen, Z.Q.[Zhi-Qiang], 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., Pang, Y., 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], Li, H.Y.[Hua-Yu], Liu, T.[Tianbi], Zheng, L.[Lin], Fan, W.G.[Wei-Guo],
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

Li, X.[Xiang], Deng, J.Y.[Jing-Yu], Fang, Y.[Yi],
Few-Shot Object Detection on Remote Sensing Images,
GeoRS(60), 2022, pp. 1-14.
IEEE DOI 2112
Object detection, Feature extraction, Remote sensing, Proposals, Learning systems, Computer architecture, Training, You-Only-Look-Once (YOLO) 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], Nguyen, T.V.[Tam V.],
Few-shot object detection via baby learning,
IVC(120), 2022, pp. 104398.
Elsevier DOI 2204
Few-shot object detection, Few-shot learning, Baby learning BibRef

Cheng, M.[Meng], Wang, H.[Hanli], 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

Qu, Z.[Zhong], Shang, X.[Xue], Xia, S.F.[Shu-Fang], Yi, T.M.[Tu-Ming], Zhou, D.Y.[Dong-Yang],
A method of single-shot target detection with multi-scale feature fusion and feature enhancement,
IET-IPR(16), No. 6, 2022, pp. 1752-1763.
DOI Link 2204
BibRef

Qu, Z.[Zhong], Gao, L.Y.[Le-Yuan], Wang, S.Y.[Sheng-Ye], Yin, H.N.[Hao-Nan], Yi, T.M.[Tu-Ming],
An improved YOLOv5 method for large objects detection with multi-scale feature cross-layer fusion network,
IVC(125), 2022, pp. 104518.
Elsevier DOI 2208
Object detection, Feature extraction, Feature fusion, , Autoanchor mechanism 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], Zuo, W.M.[Wang-Meng],
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, 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.[Ronghui], 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

Cappio Borlino, F.[Francesco], 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

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.[Caifeng],
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.[Yuewen], Feng, W.[Wenquan], 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

Xia, R.Y.[Rui-Yang], Li, G.Q.[Guo-Quan], Huang, Z.W.[Zheng-Wen], Meng, H.Y.[Hong-Ying], Pang, Y.[Yu],
Bi-path Combination YOLO for Real-time Few-shot Object Detection,
PRL(165), 2023, pp. 91-97.
Elsevier DOI 2301
Few-shot object detection, Transfer learning, Real-time, Bi-path Combination, You Only Look Once, Attentive DropBlock 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


Wang, C.Z.[Chun-Zhi], Tong, X.[Xin], Zhu, J.H.[Jia-Hui], Gao, R.[Rong],
Ghost-YOLOX: A Lightweight and Efficient Implementation of Object Detection Model,
ICPR22(4552-4558)
IEEE DOI 2212
Training, Image segmentation, Costs, Convolution, Fuses, Computational modeling, Object detection, Object detection, Multi-scale pyramidal convolution 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

Wu, S.[Shuang], Pei, W.J.[Wen-Jie], Mei, D.[Dianwen], Chen, F.L.[Fang-Lin], Tian, J.[Jiandong], Lu, G.M.[Guang-Ming],
Multi-faceted Distillation of Base-Novel Commonality for Few-Shot Object Detection,
ECCV22(IX:578-594).
Springer DOI 2211
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

Hong, S.[Sunghwan], Cho, S.[Seokju], Nam, J.[Jisu], Lin, S.[Stephen], Kim, S.[Seungryong],
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation,
ECCV22(XXIX:108-126).
Springer DOI 2211
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

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

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, Pattern recognition BibRef

Zhang, S.[Shan], Wang, L.[Lei], Murray, N.[Naila], Koniusz, P.[Piotr],
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

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

Sui, L.[Lin], Zhang, C.L.[Chen-Lin], Wu, J.X.[Jian-Xin],
Salvage of Supervision in Weakly Supervised Object Detection,
CVPR22(14207-14216)
IEEE DOI 2210
Bridges, Visualization, Machine vision, Object detection, Pattern recognition, Noise measurement, Recognition: detection, Self- semi- meta- Vision applications and systems 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

Wu, P.Y.[Ping-Yu], Zhai, W.[Wei], Cao, Y.[Yang],
Background Activation Suppression for Weakly Supervised Object Localization,
CVPR22(14228-14237)
IEEE DOI 2210
Location awareness, Correlation, Codes, Generators, Pattern recognition, Task analysis, Recognition: detection, Self- semi- meta- unsupervised learning BibRef

Xu, Y.Q.[Yun-Qiu], Sun, Y.F.[Yi-Fan], Yang, Z.X.[Zong-Xin], Miao, J.[Jiaxu], 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

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

Huang, P.L.[Pei-Liang], Han, J.W.[Jun-Wei], Cheng, D.[De], Zhang, D.W.[Ding-Wen],
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

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

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

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, Pattern recognition, 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, Pattern recognition, 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, Pattern recognition, 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, Pattern recognition, 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.[Jianan], 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, Pattern recognition 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, Pattern recognition 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, Pattern recognition 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

Wang, H.C.[Hao-Chen], Yang, Y.D.[Yan-Dan], Cao, X.B.[Xian-Bin], Zhen, X.T.[Xian-Tong], Snoek, C.[Cees], 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

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

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

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.[Yuwei], Meng, F.[Fanman], 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

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
Semi-Supervised Object Detection .


Last update:Jan 29, 2023 at 20:54:24