7.1.10.1.1 Weakly Supervised, Unsupervised Salient Regions

Chapter Contents (Back)
Salient Regions. Salient Objects. Object Detection. Weakly Supervised.
See also Semi-Supervised Object Detection.
See also Salient Regions, Saliencey for Regions.
See also Color, Multispectral, RGB, for Salient Regions.

Cholakkal, H.[Hisham], Johnson, J.[Jubin], Rajan, D.[Deepu],
A classifier-guided approach for top-down salient object detection,
SP:IC(45), No. 1, 2016, pp. 24-40.
Elsevier DOI 1605
Saliency BibRef

Tang, P.[Peng], Wang, X.G.[Xing-Gang], Huang, Z.L.[Zi-Long], Bai, X.[Xiang], Liu, W.Y.[Wen-Yu],
Deep patch learning for weakly supervised object classification and discovery,
PR(71), No. 1, 2017, pp. 446-459.
Elsevier DOI 1707
Patch, feature, learning BibRef

Cholakkal, H.[Hisham], Johnson, J.[Jubin], Rajan, D.[Deepu],
Backtracking Spatial Pyramid Pooling-Based Image Classifier for Weakly Supervised Top-Down Salient Object Detection,
IP(27), No. 12, December 2018, pp. 6064-6078.
IEEE DOI 1810
convolution, feature extraction, feedforward neural nets, image classification, object detection, probability, CNN image classifier BibRef

Liu, L.C.[Liang-Chen], Wiliem, A.[Arnold], Chen, S.K.[Shao-Kang], Lovell, B.C.[Brian C.],
What is the best way for extracting meaningful attributes from pictures?,
PR(64), No. 1, 2017, pp. 314-326.
Elsevier DOI 1701
Visual attribute measure visual attribute meaningfulness. BibRef

Yang, S.Q.[Si-Qi], Wu, L.[Lin], Wiliem, A.[Arnold], Lovell, B.C.[Brian C.],
Unsupervised Domain Adaptive Object Detection Using Forward-backward Cyclic Adaptation,
ACCV20(III:124-142).
Springer DOI 2103
BibRef

Quan, R.[Rong], Han, J.W.[Jun-Wei], Zhang, D.W.[Ding-Wen], Nie, F.P.[Fei-Ping], Qian, X.M.[Xue-Ming], Li, X.L.[Xue-Long],
Unsupervised Salient Object Detection via Inferring from Imperfect Saliency Models,
MultMed(20), No. 5, May 2018, pp. 1101-1112.
IEEE DOI 1805
Analytical models, Computational complexity, Computational modeling, Fuses, Labeling, Object detection, weak prediction BibRef

Hsu, K., Lin, Y., Chuang, Y.,
Weakly Supervised Salient Object Detection by Learning A Classifier-Driven Map Generator,
IP(28), No. 11, November 2019, pp. 5435-5449.
IEEE DOI 1909
Saliency detection, Generators, Training data, Training, Feature extraction, Proposals, Task analysis, weakly supervised learning BibRef

Luo, A.[Ao], Li, X.[Xin], Yang, F.[Fan], Jiao, Z.C.[Zhi-Cheng], Cheng, H.[Hong],
Webly-supervised learning for salient object detection,
PR(103), 2020, pp. 107308.
Elsevier DOI 2005
Salient object detection, Webly-supervised learning, Deep learning BibRef

Zhang, M.[Ming], Zeng, B.[Bing],
A progressive learning framework based on single-instance annotation for weakly supervised object detection,
CVIU(193), 2020, pp. 102903.
Elsevier DOI 2003
Single-instance annotation, Progressive learning framework, Weakly supervised object detection, Instance mining BibRef

Liu, Y.X.[Yu-Xuan], Wang, P.J.[Peng-Jie], Cao, Y.[Ying], Liang, Z.J.[Zi-Jian], Lau, R.W.H.[Rynson W. H.],
Weakly-Supervised Salient Object Detection With Saliency Bounding Boxes,
IP(30), 2021, pp. 4423-4435.
IEEE DOI 2104
Object detection, Annotations, Detectors, Training, Proposals, Computer science, Task analysis, Saliency bounding boxes, weak supervision BibRef

Zheng, X.Y.[Xiao-Yang], Tan, X.[Xin], Zhou, J.[Jie], Ma, L.Z.[Li-Zhuang], Lau, R.W.H.[Rynson W. H.],
Weakly-Supervised Saliency Detection via Salient Object Subitizing,
CirSysVideo(31), No. 11, November 2021, pp. 4370-4380.
IEEE DOI 2112
Saliency detection, Task analysis, Object detection, Feature extraction, Training, Image segmentation, Annotations, object subitizing BibRef

Liang, Z.J.[Zi-Jian], Wang, P.J.[Peng-Jie], Xu, K.[Ke], Zhang, P.P.[Ping-Ping], Lau, R.W.H.[Rynson W. H.],
Weakly-Supervised Salient Object Detection on Light Fields,
IP(31), 2022, pp. 6295-6305.
IEEE DOI 2210
Light fields, Saliency detection, Object detection, Annotations, Visualization, Image color analysis, Feature extraction, weak supervision BibRef

He, S.F.[Sheng-Feng], Lau, R.W.H.[Rynson W. H.],
Exemplar-Driven Top-Down Saliency Detection via Deep Association,
CVPR16(5723-5732)
IEEE DOI 1612
BibRef
Earlier:
Saliency Detection with Flash and No-flash Image Pairs,
ECCV14(III: 110-124).
Springer DOI 1408
Only closer (foreground or salient) objects are well lit by the flash. BibRef

Zhou, Z.H.[Zhi-Heng], Guo, Y.F.[Yong-Fan], Dai, M.[Ming], Huang, J.C.[Jun-Chu], Li, X.W.[Xiang-Wei],
Weakly Supervised Salient Object Detection Via Double Object Proposals Guidance,
IET-IPR(15), No. 9, 2021, pp. 1957-1970.
DOI Link 2106
BibRef

Dong, B.[Bowen], Huang, Z.T.[Zi-Tong], Guo, Y.L.[Yue-Lin], Wang, Q.L.[Qi-Long], Niu, Z.X.[Zhen-Xing], Zuo, W.M.[Wang-Meng],
Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters,
ICCV21(2856-2865)
IEEE DOI 2203
Training, Location awareness, Codes, Computational modeling, Object detection, Classification algorithms, BibRef

Zhang, L.[Libao], Ma, J.[Jie],
Salient Object Detection Based on Progressively Supervised Learning for Remote Sensing Images,
GeoRS(59), No. 11, November 2021, pp. 9682-9696.
IEEE DOI 2111
Annotations, Training, Supervised learning, Remote sensing, Object detection, Image segmentation, Feature extraction, weakly supervised learning (WSL) BibRef

Liu, Y.[Yan], Zhang, Y.Z.[Yun-Zhou], Wang, Z.Y.[Zhen-Yu], Yang, F.[Fei], Qin, C.[Cao], Qiu, F.[Feng], Coleman, S.[Sonya], Kerr, D.[Dermot],
Complementary Characteristics Fusion Network for Weakly Supervised Salient Object Detection,
IVC(126), 2022, pp. 104536.
Elsevier DOI 2209
Salient object detection, Weakly supervised learning, Edge fusion module, Feature correlation module, Self-supervised salient detection loss BibRef

Yang, J.[Jie], Shi, Y.[Yong], Qi, Z.Q.[Zhi-Quan],
Learning deep feature correspondence for unsupervised anomaly detection and segmentation,
PR(132), 2022, pp. 108874.
Elsevier DOI 2209
Anomaly detection, Anomaly segmentation, Feature correspondence, Dual network BibRef

Gonthier, N.[Nicolas], Ladjal, S.[Saďd], Gousseau, Y.[Yann],
Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts,
CVIU(214), 2022, pp. 103299.
Elsevier DOI 2112
Deep learning, Convolutional neural networks, Weakly supervised object detection, Non-photographic images, Multiple instance learning BibRef

Wu, Z.H.[Zhi-Hao], Liu, C.L.[Cheng-Liang], Wen, J.[Jie], Xu, Y.[Yong], Yang, J.[Jian], Li, X.L.[Xue-Long],
Selecting High-Quality Proposals for Weakly Supervised Object Detection With Bottom-Up Aggregated Attention and Phase-Aware Loss,
IP(32), 2023, pp. 682-693.
IEEE DOI 2301
Proposals, Training, Object detection, Loss measurement, Detectors, Feature extraction, Phase measurement, high-quality supervision BibRef

Zhou, H.J.[Hua-Jun], Chen, P.J.[Pei-Jia], Yang, L.X.[Ling-Xiao], Xie, X.H.[Xiao-Hua], Lai, J.H.[Jian-Huang],
Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection,
CirSysVideo(33), No. 2, February 2023, pp. 743-755.
IEEE DOI 2302
Detectors, Feature extraction, Semantics, Training, Object detection, Task analysis, Random access memory, adaptive decision boundary BibRef

Zhou, H.J.[Hua-Jun], Qiao, B.[Bo], Yang, L.X.[Ling-Xiao], Lai, J.H.[Jian-Huang], Xie, X.H.[Xiao-Hua],
Texture-Guided Saliency Distilling for Unsupervised Salient Object Detection,
CVPR23(7257-7267)
IEEE DOI 2309
BibRef

Cong, R.M.[Run-Min], Qin, Q.[Qi], Zhang, C.[Chen], Jiang, Q.P.[Qiu-Ping], Wang, S.Q.[Shi-Qi], Zhao, Y.[Yao], Kwong, S.[Sam],
A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels,
CirSysVideo(33), No. 2, February 2023, pp. 534-548.
IEEE DOI 2302
Training, Task analysis, Object detection, Decoding, Annotations, Urban areas, Information science, Salient object detection, group-wise incremental mechanism BibRef

Pang, Y.[Yu], Wu, C.[Chengdong], Wu, H.[Hao], Yu, X.[Xiaosheng],
Unsupervised Multi-Subclass Saliency Classification for Salient Object Detection,
MultMed(25), 2023, pp. 2189-2202.
IEEE DOI 2306
Training, Object detection, Task analysis, Predictive models, Automobiles, Saliency detection, Manuals, spatial smoothness BibRef

Liu, Z.F.[Zhou-Feng], Wang, K.H.[Kai-Hua], Li, C.L.[Chun-Lei], Ding, S.M.[Shun-Min], Xi, J.T.[Jiang-Tao],
Triple critical feature capture network: A triple critical feature capture network for weakly supervised object detection,
IET-CV(17), No. 8, 2023, pp. 895-912.
DOI Link 2312
computer vision, image processing, object detection BibRef

Xu, B.W.[Bin-Wei], Liang, H.R.[Hao-Ran], Gong, W.H.[Wei-Hua], Liang, R.H.[Rong-Hua], Chen, P.[Peng],
A Visual Representation-Guided Framework With Global Affinity for Weakly Supervised Salient Object Detection,
CirSysVideo(34), No. 1, January 2024, pp. 248-259.
IEEE DOI 2401
BibRef

Wu, Z.H.[Zhi-Hao], Wen, J.[Jie], Xu, Y.[Yong], Yang, J.[Jian], Zhang, D.[David],
Multiple Instance Detection Networks With Adaptive Instance Refinement,
MultMed(25), 2023, pp. 267-279.
IEEE DOI 2301
Proposals, Training, Annotations, Object detection, Adaptive systems, Benchmark testing, Detectors, Weakly supervised object detection, proposal score BibRef

Zhang, H.[Han], Wang, Y.F.[Yong-Fang], Yang, Y.J.[Ying-Jie],
LL-WSOD: Weakly supervised object detection in low-light,
JVCIR(98), 2024, pp. 104010.
Elsevier DOI 2402
Weakly supervised learning, Object detection, Low light, Salient priors BibRef


Ravindran, S.[Sriram], Basu, D.[Debraj],
Sempart: Self-supervised Multi-resolution Partitioning of Image Semantics,
ICCV23(723-733)
IEEE DOI 2401
BibRef

Veksler, O.[Olga],
Test Time Adaptation with Regularized Loss for Weakly Supervised Salient Object Detection,
CVPR23(7360-7369)
IEEE DOI 2309
BibRef

Lin, Z.W.[Zhi-Wei], Yang, Z.Y.[Zeng-Yu], Wang, Y.T.[Yong-Tao],
Foreground Guidance and Multi-Layer Feature Fusion for Unsupervised Object Discovery with Transformers,
WACV23(4032-4042)
IEEE DOI 2302

WWW Link. Location awareness, Visualization, Aggregates, Detectors, Object detection, Transformers BibRef

Wang, Y.F.[Yi-Fan], Zhang, W.B.[Wen-Bo], Wang, L.J.[Li-Jun], Liu, T.[Ting], Lu, H.C.[Hu-Chuan],
Multi-Source Uncertainty Mining for Deep Unsupervised Saliency Detection,
CVPR22(11717-11726)
IEEE DOI 2210
Adaptation models, Uncertainty, Computer network reliability, Training data, Object detection, Reliability engineering, Self- semi- meta- unsupervised learning BibRef

Wang, Z.D.[Zhen-Dong], Chen, Z.[Zhenyuan], Gong, C.[Chen],
Class Activation Map Refinement via Semantic Affinity Exploration for Weakly Supervised Object Detection,
ICIP22(4168-4172)
IEEE DOI 2211
Location awareness, Semantics, Collaboration, Object detection, Detectors, Benchmark testing, Market research, class activation map BibRef

Hou, L.[Luwei], Zhang, Y.[Yu], Fu, K.[Kui], Li, J.[Jia],
Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection,
CVPR21(9924-9933)
IEEE DOI 2111
Annotations, Collaboration, Object detection, Detectors, Generators, Pattern recognition BibRef

Li, L.T.B.[Lv Tang Bo], Zhong, Y.J.[Yi-Jie], Ding, S.H.[Shou-Hong], Song, M.[Mofei],
Disentangled High Quality Salient Object Detection,
ICCV21(3560-3570)
IEEE DOI 2203
Training, Deep learning, Uncertainty, Semantics, Refining, Graphics processing units, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Ouyang, S.X.[Sheng-Xiong], Wang, X.L.[Xing-Lu], Lyu, K.[Kejie], Li, Y.M.[Ying-Ming],
Pseudo-Label Generation-Evaluation Framework for Cross Domain Weakly Supervised Object Detection,
ICIP21(724-728)
IEEE DOI 2201
Annotations, Image processing, Detectors, Object detection, Task analysis, Cross domain, Weakly supervised, Object detection, Evaluator BibRef

Shen, Y.H.[Yun-Hang], Ji, R.R.[Rong-Rong], Wang, Y.[Yan], Chen, Z.W.[Zhi-Wei], Zheng, F.[Feng], Huang, F.Y.[Fei-Yue], Wu, Y.S.[Yun-Sheng],
Enabling Deep Residual Networks for Weakly Supervised Object Detection,
ECCV20(VIII:118-136).
Springer DOI 2011
BibRef

Kosugi, S., Yamasaki, T., Aizawa, K.,
Object-Aware Instance Labeling for Weakly Supervised Object Detection,
ICCV19(6063-6071)
IEEE DOI 2004
image annotation, image classification, iterative methods, learning (artificial intelligence), object detection, Focusing BibRef

Inoue, N., Furuta, R., Yamasaki, T., Aizawa, K.,
Cross-Domain Weakly-Supervised Object Detection Through Progressive Domain Adaptation,
CVPR18(5001-5009)
IEEE DOI 1812
Object detection, Task analysis, Detectors, Dogs, Search engines, Feature extraction, Noise measurement BibRef

Zhang, D., Han, J., Zhang, Y.,
Supervision by Fusion: Towards Unsupervised Learning of Deep Salient Object Detector,
ICCV17(4068-4076)
IEEE DOI 1802
convolution, image fusion, neural nets, object detection, unsupervised learning, DNNs, deep convolutional models, Unsupervised learning BibRef

López-Sastre, R.J.[Roberto J.],
Unsupervised Robust Feature-Based Partition Ensembling to Discover Categories,
Robust16(1187-1195)
IEEE DOI 1612
Different features to aggregate different partitions to get objects. BibRef

Cholakkal, H.[Hisham], Johnson, J.[Jubin], Rajan, D.[Deepu],
Backtracking ScSPM Image Classifier for Weakly Supervised Top-Down Saliency,
CVPR16(5278-5287)
IEEE DOI 1612
BibRef

Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Color, Multispectral, RGB, for Salient Regions .


Last update:Mar 16, 2024 at 20:36:19