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
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
Song, Y.[Yaoye],
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,
Electronics packaging, 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
BibRef
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
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.[Yiran],
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.[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.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, Pattern recognition, 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, 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 .