7.1.7.9 Dense Object Detection

Chapter Contents (Back)
Object Detection. Dense Objects.
See also Counting Instances, Counting Objects.

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, 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.[Dongli], Deng, J.[Jinhong], 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.[Yifan], 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, Pattern recognition, object counting BibRef

Li, X.[Xiang], Wang, W.[Wenhai], 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

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, Pattern recognition 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, Computer architecture, 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
Interest Operators, Interest Points, Feature Points, Salient Points .


Last update:Nov 28, 2022 at 16:32:47