Goldman, E.[Eran],
Goldberger, J.[Jacob],
CRF with deep class embedding for large scale classification,
CVIU(191), 2020, pp. 102865.
Elsevier DOI
2002
CRF, Class embedding, Matrix factorization,
Surrogate likelihood, Batch normalization
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Goldman, E.[Eran],
Herzig, R.[Roei],
Eisenschtat, A.[Aviv],
Goldberger, J.[Jacob],
Hassner, T.[Tal],
Precise Detection in Densely Packed Scenes,
CVPR19(5222-5231).
IEEE DOI
2002
E.g. man-made scenes with numerous identical objects.
BibRef
Zhou, L.M.[Lin-Mao],
Chang, H.[Hong],
Ma, B.P.[Bing-Peng],
Shan, S.G.[Shi-Guang],
Interactive Regression and Classification for Dense Object Detector,
IP(31), 2022, pp. 3684-3696.
IEEE DOI
2206
Location awareness, Detectors, Feature extraction,
Object detection, Standards, Backpropagation, Pipelines, interactive
BibRef
Yang, R.P.[Rui-Ping],
Yu, J.[Jiguo],
Yin, J.[Jian],
Liu, K.[Kun],
Xu, S.H.[Shao-Hua],
A dense R-CNN multi-target instance segmentation model and its
application in medical image processing,
IET-IPR(16), No. 9, 2022, pp. 2495-2505.
DOI Link
2206
BibRef
Li, X.[Xiang],
Lv, C.Q.[Cheng-Qi],
Wang, W.H.[Wen-Hai],
Li, G.[Gang],
Yang, L.F.[Ling-Feng],
Yang, J.[Jian],
Generalized Focal Loss: Towards Efficient Representation Learning for
Dense Object Detection,
PAMI(45), No. 3, March 2023, pp. 3139-3153.
IEEE DOI
2302
Location awareness, Detectors, Estimation, Training,
Predictive models, Feature extraction, Object detection, deep learning
BibRef
Li, X.[Xiang],
Wang, W.H.[Wen-Hai],
Hu, X.L.[Xiao-Lin],
Li, J.[Jun],
Tang, J.H.[Jin-Hui],
Yang, J.[Jian],
Generalized Focal Loss V2: Learning Reliable Localization Quality
Estimation for Dense Object Detection,
CVPR21(11627-11636)
IEEE DOI
2111
Location awareness, Training, Uncertainty,
Correlation, Estimation, Object detection
BibRef
Wang, S.Y.[Sheng-Ye],
Qu, Z.[Zhong],
Li, C.J.[Cui-Jin],
A Dense-Aware Cross-splitNet for Object Detection and Recognition,
CirSysVideo(33), No. 5, May 2023, pp. 2290-2301.
IEEE DOI
2305
Object detection, Feature extraction, Detectors, Task analysis,
Image recognition, Head, Convolution, Object detection, cross-splitNet
BibRef
Chu, K.C.[Kuan-Chao],
Nakayama, H.[Hideki],
Two-Path Object Knowledge Injection for Detecting Novel Objects with
Single-Stage Dense Detector,
IEICE(E106-D), No. 11, November 2023, pp. 1868-1880.
WWW Link.
2311
BibRef
Song, Y.Y.[Yao-Ye],
Zhang, P.[Peng],
Huang, W.[Wei],
Zha, Y.F.[Yu-Fei],
You, T.[Tao],
Zhang, Y.N.[Yan-Ning],
Closed-loop unified knowledge distillation for dense object detection,
PR(149), 2024, pp. 110235.
Elsevier DOI
2403
Triple parallel distillation,
Hierarchical re-weighting attention distillation, Closed-loop unified
BibRef
Liu, C.[Chang],
Li, X.M.[Xiao-Mao],
Xiao, W.P.[Wei-Ping],
Xie, S.R.[Shao-Rong],
CCDet: Confidence-Consistent Learning for Dense Object Detection,
IP(33), 2024, pp. 2746-2758.
IEEE DOI
2404
Location awareness, Task analysis, Estimation, Detectors,
Feature extraction, label assignment
BibRef
Ma, J.W.[Jia-Wei],
Liang, M.[Min],
Chen, L.[Lei],
Tian, S.[Shu],
Chen, S.L.[Song-Lu],
Qin, J.Y.[Jing-Yan],
Yin, X.C.[Xu-Cheng],
Sample Weighting with Hierarchical Equalization Loss for Dense Object
Detection,
MultMed(26), 2024, pp. 5846-5859.
IEEE DOI
2404
Detectors, Task analysis, Object detection, Training,
Location awareness, Feature extraction, Modulation, weighted loss
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Lu, Y.X.[Yu-Xiang],
Sirejiding, S.[Shalayiding],
Ding, Y.[Yue],
Wang, C.L.[Chun-Lin],
Lu, H.T.[Hong-Tao],
Prompt Guided Transformer for Multi-Task Dense Prediction,
MultMed(26), 2024, pp. 6375-6385.
IEEE DOI
2404
Task analysis, Transformers, Decoding, Multitasking, Adaptation models,
Feature extraction, Tuning, Multi-task learning, vision transformer
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Wu, H.X.[Hui-Xin],
Zhu, Y.[Yang],
Cao, M.[Mengdi],
An algorithm for detecting dense small objects in aerial photography
based on coordinate position attention module,
IET-IPR(18), No. 7, 2024, pp. 1759-1767.
DOI Link
2405
convolutional neural nets, image processing
BibRef
Wang, L.F.[Lin-Fei],
Zhan, Y.B.[Yi-Bing],
Lan, L.[Long],
Lin, X.[Xu],
Tao, D.P.[Da-Peng],
Gao, X.B.[Xin-Bo],
DeIoU: Toward Distinguishable Box Prediction in Densely Packed Object
Detection,
CirSysVideo(34), No. 11, November 2024, pp. 11086-11100.
IEEE DOI
2412
Object detection, Predictive models, Detectors, Interference,
Training, Feature extraction, Shape, Dense object detection,
intersection over union
BibRef
Zhang, L.[Li],
Lu, J.C.[Jia-Chen],
Zheng, S.X.[Si-Xiao],
Zhao, X.X.[Xin-Xuan],
Zhu, X.T.[Xia-Tian],
Fu, Y.W.[Yan-Wei],
Xiang, T.[Tao],
Feng, J.F.[Jian-Feng],
Torr, P.H.S.[Philip H. S.],
Vision Transformers: From Semantic Segmentation to Dense Prediction,
IJCV(132), No. 12, December 2024, pp. 6142-6162.
Springer DOI
2501
BibRef
Li, C.L.[Cheng-Long],
Zhang, J.W.[Jian-Wei],
Huo, B.[Bihan],
Xue, Y.J.[Ying-Jian],
DHQ-DETR: Distributed and High-Quality Object Query for Enhanced
Dense Detection in Remote Sensing,
RS(17), No. 3, 2025, pp. 514.
DOI Link
2502
BibRef
Ran, J.F.[Jiang-Fan],
Yan, H.B.[Hai-Bin],
Interactive shape estimation for densely cluttered objects,
PRL(191), 2025, pp. 8-14.
Elsevier DOI
2504
Shape estimation, Active exploration, Intelligent robot, Deep learning
BibRef
Song, Y.Y.[Yao-Ye],
Zhang, P.[Peng],
Huang, W.[Wei],
Zha, Y.F.[Yu-Fei],
Zhang, Y.N.[Yan-Ning],
Flexible Temperature Parallel Distillation for Dense Object
Detection: Make Response-Based Knowledge Distillation Great Again,
CirSysVideo(35), No. 5, May 2025, pp. 4963-4975.
IEEE DOI
2505
Location awareness, Object detection, Training, Feature extraction,
Detectors, Semantics, flexible temperature
BibRef
Dong, C.J.[Chao-Jun],
Wang, C.X.[Cheng-Xuan],
Zhai, Y.[Yikui],
Li, Y.[Ye],
Zhou, J.H.[Jian-Hong],
Coscia, P.[Pasquale],
Genovese, A.[Angelo],
Piuri, V.[Vincenzo],
Scotti, F.[Fabio],
GMTNet: Dense Object Detection via Global Dynamically Matching
Transformer Network,
CirSysVideo(35), No. 5, May 2025, pp. 4923-4936.
IEEE DOI Code:
WWW Link.
2505
Training, Transformers, Shape, Detectors, Adaptation models, Accuracy,
Feature extraction, Visualization, Noise, Head, Mobile window system,
industrial scenarios
BibRef
Zhang, G.[Gang],
Li, Z.[Ziyi],
Tang, C.F.[Chu-Feng],
Li, J.M.[Jian-Min],
Hu, X.L.[Xiao-Lin],
CEDNet: A cascade encoder-decoder network for dense prediction,
PR(158), 2025, pp. 111072.
Elsevier DOI Code:
WWW Link.
2411
Dense prediction, Object detection, Instance segmentation,
Semantic segmentation, Cascade encoder-decoder, Multi-scale feature fusion
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Dong, J.P.[Jin-Peng],
Yao, D.[Dingyi],
Hu, Y.F.[Yu-Feng],
Zhou, S.P.[San-Ping],
Zheng, N.N.[Nan-Ning],
A Novel Dense Object Detector With Scale Balanced Sample Assignment
and Refinement,
CirSysVideo(35), No. 9, September 2025, pp. 9337-9350.
IEEE DOI
2509
Detectors, Location awareness, Object detection, Training, Accuracy,
Transformers, Measurement, Visualization, Semantics, Multitasking,
refinement
BibRef
Li, X.M.[Xi-Ming],
Di, X.G.[Xiao-Guang],
Liu, M.[Maozhen],
Ye, S.X.[Shao-Xun],
Feature-aligned distillation for dense object detection via refined
semantic guidance and distribution consistency,
CVIU(262), 2025, pp. 104519.
Elsevier DOI
2512
Knowledge Distillation, Object detection, Feature alignment,
Refined semantic guidance, Distribution consistency
BibRef
Wang, J.J.[Jun-Jie],
Chen, B.[Bin],
Li, Y.L.[Yu-Lin],
Kang, B.[Bin],
Chen, Y.[Yichi],
Tian, Z.[Zhuotao],
DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception,
CVPR25(14824-14834)
IEEE DOI Code:
WWW Link.
2508
Visualization, Limiting, Correlation, Foundation models,
Semantic segmentation, Crops, Object detection, Predictive models,
dense prediction tasks
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Xia, C.[Chunlong],
Wang, X.L.[Xin-Liang],
Lv, F.[Feng],
Hao, X.[Xin],
Shi, Y.F.[Yi-Feng],
ViT-CoMer: Vision Transformer with Convolutional Multi-scale Feature
Interaction for Dense Predictions,
CVPR24(5493-5502)
IEEE DOI Code:
WWW Link.
2410
Convolutional codes, Semantic segmentation, Semantics,
Training data, Object detection,
DensePrediction
BibRef
Zu, S.C.[Shi-Cheng],
Jin, Y.C.[Yu-Cheng],
Rank and Sort Loss-Aware Label Assignment with Centroid Prior for
Dense Object Detection,
FG24(1-9)
IEEE DOI
2408
Location awareness, Training, Costs, Shape, Semantics,
Object detection, Gesture recognition
BibRef
Zhang, S.L.[Shi-Long],
Wang, X.J.[Xin-Jiang],
Wang, J.Q.[Jia-Qi],
Pang, J.M.[Jiang-Miao],
Lyu, C.Q.[Cheng-Qi],
Zhang, W.W.[Wen-Wei],
Luo, P.[Ping],
Chen, K.[Kai],
Dense Distinct Query for End-to-End Object Detection,
CVPR23(7329-7338)
IEEE DOI
2309
BibRef
Li, S.[Shuai],
Li, M.H.[Ming-Han],
Li, R.H.[Rui-Huang],
He, C.H.[Chen-Hang],
Zhang, L.[Lei],
One-to-Few Label Assignment for End-to-End Dense Detection,
CVPR23(7350-7359)
IEEE DOI
2309
BibRef
Stegmüller, T.[Thomas],
Lebailly, T.[Tim],
Bozorgtabar, B.[Behzad],
Tuytelaars, T.[Tinne],
Thiran, J.P.[Jean-Philippe],
CrOC: Cross-View Online Clustering for Dense Visual Representation
Learning,
CVPR23(7000-7009)
IEEE DOI
2309
BibRef
Borse, S.[Shubhankar],
Das, D.[Debasmit],
Park, H.[Hyojin],
Cai, H.[Hong],
Garrepalli, R.[Risheek],
Porikli, F.M.[Fatih M.],
DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction,
CVPR23(19466-19477)
IEEE DOI
2309
BibRef
Yin, D.S.[Dong-Shuo],
Yang, Y.R.[Yi-Ran],
Wang, Z.C.[Zhe-Chao],
Yu, H.F.[Hong-Feng],
Wei, K.W.[Kai-Wen],
Sun, X.[Xian],
1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense
Predictions,
CVPR23(20116-20126)
IEEE DOI
2309
BibRef
Yang, C.Y.[Chenhong-Yi],
Ochal, M.[Mateusz],
Storkey, A.[Amos],
Crowley, E.J.[Elliot J.],
Prediction-Guided Distillation for Dense Object Detection,
ECCV22(IX:123-138).
Springer DOI
2211
BibRef
Xu, D.L.[Dong-Li],
Deng, J.H.[Jin-Hong],
Li, W.[Wen],
Revisiting AP Loss for Dense Object Detection:
Adaptive Ranking Pair Selection,
CVPR22(14167-14176)
IEEE DOI
2210
Location awareness, Codes, Focusing, Clustering algorithms,
Object detection, Detectors, Recognition: detection,
Vision applications and systems
BibRef
Zheng, Z.H.[Zhao-Hui],
Ye, R.G.[Rong-Guang],
Wang, P.[Ping],
Ren, D.W.[Dong-Wei],
Zuo, W.M.[Wang-Meng],
Hou, Q.[Qibin],
Cheng, M.M.[Ming-Ming],
Localization Distillation for Dense Object Detection,
CVPR22(9397-9406)
IEEE DOI
2210
Location awareness, Training, Schedules, Semantics, Object detection,
Detectors, Recognition: detection, categorization, retrieval
BibRef
Shu, C.Y.[Chang-Yong],
Liu, Y.F.[Yi-Fan],
Gao, J.F.[Jian-Fei],
Yan, Z.[Zheng],
Shen, C.H.[Chun-Hua],
Channel-wise Knowledge Distillation for Dense Prediction*,
ICCV21(5291-5300)
IEEE DOI
2203
Training, Semantics, Estimation, Object detection, Detectors,
Predictive models, Efficient training and inference methods,
grouping and shape
BibRef
Deng, Z.L.[Zhao-Li],
Yang, C.[Chenhui],
Multiple-step Sampling for Dense Object Detection and Counting,
ICPR21(1036-1042)
IEEE DOI
2105
Training, Detectors, Object detection, Benchmark testing,
Sampling methods, Feature extraction, object counting
BibRef
Hu, H.Z.[Han-Zhe],
Bai, S.[Shuai],
Li, A.[Aoxue],
Cui, J.S.[Jin-Shi],
Wang, L.W.[Li-Wei],
Dense Relation Distillation with Context-aware Aggregation for
Few-Shot Object Detection,
CVPR21(10180-10189)
IEEE DOI
2111
Training, Deep learning, Adaptation models, Codes,
Annotations, Object detection
BibRef
Gao, Z.T.[Zi-Teng],
Wang, L.M.[Li-Min],
Wu, G.S.[Gang-Shan],
Mutual Supervision for Dense Object Detection,
ICCV21(3621-3630)
IEEE DOI
2203
Training, Location awareness, Pipelines, Detectors, Object detection,
Benchmark testing, Detection and localization in 2D and 3D,
BibRef
Li, B.[Bo],
Yao, Y.Q.[Yong-Qiang],
Tan, J.R.[Jing-Ru],
Zhang, G.[Gang],
Yu, F.W.[Feng-Wei],
Lu, J.W.[Jian-Wei],
Luo, Y.[Ye],
Equalized Focal Loss for Dense Long-Tailed Object Detection,
CVPR22(6980-6989)
IEEE DOI
2210
Training, Industries, Deep learning, Pipelines, Detectors,
Object detection, Transfer/low-shot/long-tail learning, retrieval
BibRef
Tan, J.R.[Jing-Ru],
Lu, X.[Xin],
Zhang, G.[Gang],
Yin, C.Q.[Chang-Qing],
Li, Q.Q.[Quan-Quan],
Equalization Loss v2:
A New Gradient Balance Approach for Long-tailed Object Detection,
CVPR21(1685-1694)
IEEE DOI
2111
Training, Codes, Object detection,
Benchmark testing, Boosting
BibRef
Kim, H.[Hanjae],
Joung, S.[Sunghun],
Kim, I.J.[Ig-Jae],
Sohn, K.H.[Kwang-Hoon],
Shape-Adaptive Kernel Network for Dense Object Detection,
ICIP20(2046-2050)
IEEE DOI
2011
Kernel, Shape, Object detection, Detectors, Convolution,
Feature extraction, Strain, Dense object detection,
object deformation
BibRef
Qiu, H.[Han],
Ma, Y.C.[Yu-Chen],
Li, Z.M.[Ze-Ming],
Liu, S.T.[Song-Tao],
Sun, J.[Jian],
BorderDet: Border Feature for Dense Object Detection,
ECCV20(I:549-564).
Springer DOI
2011
A point-like feature to guide the border search, for dense collection
of objects.
BibRef
Varadarajan, S.[Srikrishna],
Kant, S.[Sonaal],
Srivastava, M.M.[Muktabh Mayank],
Benchmark for Generic Product Detection:
A Low Data Baseline for Dense Object Detection,
ICIAR20(I:30-41).
Springer DOI
2007
Generic detection, to use across applications.
BibRef
Zhang, H.Y.[Hao-Yang],
Wang, Y.[Ying],
Dayoub, F.[Feras],
Sünderhauf, N.[Niko],
VarifocalNet: An IoU-aware Dense Object Detector,
CVPR21(8510-8519)
IEEE DOI
2111
Location awareness, Training, Codes, Detectors,
Benchmark testing
BibRef
Chen, X.,
Girshick, R.,
He, K.,
Dollar, P.,
TensorMask: A Foundation for Dense Object Segmentation,
ICCV19(2061-2069)
IEEE DOI
2004
convolutional neural nets, image segmentation, object detection,
tensors, Mask R-CNN, dense sliding-window instance segmentation,
Image segmentation
BibRef
Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Fisheye Camera, Fisheye Image Detection, Analysis, Rectification .