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Progressive Representation Adaptation for Weakly Supervised Object Localization [article]

Dong Li, Jia-Bin Huang, Yali Li, Shengjin Wang, Ming-Hsuan Yang
2017 arXiv   pre-print
We address the problem of weakly supervised object localization where only image-level annotations are available for training object detectors.  ...  In this paper, we propose to overcome these drawbacks by progressive representation adaptation with two main steps: 1) classification adaptation and 2) detection adaptation.  ...  In this paper, we present a progressive representation adaptation approach to tackle the weakly supervised object localization problem.  ... 
arXiv:1710.04647v1 fatcat:brn2iupd7re5hb3fhjq2q3i5pq

Weakly Supervised Object Localization with Progressive Domain Adaptation

Dong Li, Jia-Bin Huang, Yali Li, Shengjin Wang, Ming-Hsuan Yang
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
We address the problem of weakly supervised object localization where only image-level annotations are available for training.  ...  In this paper, we address this problem by progressive domain adaptation with two main steps: classification adaptation and detection adaptation.  ...  We find that progressive adaptation is particularly important for the weakly supervised object localization problem.  ... 
doi:10.1109/cvpr.2016.382 dblp:conf/cvpr/LiHLW016 fatcat:i7ttpgaipvdwreco7kdbefj5zm

Weakly Supervised Object Localization and Detection: A Survey [article]

Dingwen Zhang, Junwei Han, Gong Cheng, Ming-Hsuan Yang
2021 arXiv   pre-print
As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation computer vision systems  ...  supervised object localization and detection methods, and potential future directions to further promote the development of this research field.  ...  In this survey, we mainly focus on reviewing the research progress in weakly supervised object localization and detection, i.e., the red dot in the top block.  ... 
arXiv:2104.07918v1 fatcat:dwl6sjfzibdilnvjnrbifp4uke

WALLACE: Weakly Supervised Learning of Deep Convolutional Neural Networks with Multiscale Evidence

Yongsheng Liu, Wenyu Chen, Hong Qu, Tianlei Wang, Jiangzhou Ji, Kebin Miao
2020 IEEE Access  
INDEX TERMS Weakly supervised learning, convolutional neural networks, object localization, object classification, multiscale features.  ...  Extensive experiments on object classification and weakly supervised pointwise object localization show that WALLACE achieves state-of-the-art results on the VOC 2007 and VOC 2012 benchmark without bells  ...  For weakly supervised pointwise object detection, in order to perform VOLUME 8, 2020 localization, we need to generate some largest connected segment in the image and its associated object category,  ... 
doi:10.1109/access.2020.2968545 fatcat:gfyc47ou6ragfg336xb2niqqdi

Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization [article]

Wenju Xu and Yuanwei Wu and Wenchi Ma and Guanghui Wang
2019 arXiv   pre-print
In this paper, we address the problem of weakly supervised object localization (WSL), which trains a detection network on the dataset with only image-level annotations.  ...  Several strategies are taken to adaptively eliminate the noisy proposals and generate pseudo object-level annotations for the weakly labeled dataset.  ...  As a follow-up study, it is desire to adapt a new feature extraction method for the weakly supervised localization task.  ... 
arXiv:1910.02101v2 fatcat:yeaqlgzpavhvlet5iqby33bmtu

Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos [article]

Jie Wu, Guanbin Li, Xiaoguang Han, Liang Lin
2020 arXiv   pre-print
To the best of our knowledge, we offer the first attempt to extend RL to temporal localization task with weak supervision.  ...  In this paper, we propose a Boundary Adaptive Refinement (BAR) framework that resorts to reinforcement learning (RL) to guide the process of progressively refining the temporal boundary.  ...  This work can be regarded as the first attempt to extend RL to weakly supervised temporal localization tasks.  ... 
arXiv:2009.08614v1 fatcat:kbb5c5y2bjbhhjvfrgrnotkkla

A Survey of Visual Sensory Anomaly Detection [article]

Xi Jiang, Guoyang Xie, Jinbao Wang, Yong Liu, Chengjie Wang, Feng Zheng, Yaochu Jin
2022 arXiv   pre-print
Furthermore, we classify each kind of anomaly according to the level of supervision. Finally, we summarize the challenges and provide open directions for this community.  ...  However, no thorough review has been provided to summarize this area for the computer vision community.  ...  Weakly supervised method is more realistic for its more inclusive data setting, while domain adaptation is another interesting research in AD.  ... 
arXiv:2202.07006v1 fatcat:2bqzmmrnjzggti5tcewa3mh3sa

Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer

Yuxing Tang, Josiah Wang, Boyang Gao, Emmanuel Dellandrea, Robert Gaizauskas, Liming Chen
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
a semi-supervised setting.  ...  Deep CNN-based object detection systems have achieved remarkable success on several large-scale object detection benchmarks.  ...  semantic domains to adapt image classifiers into object detectors in a semi-supervised manner.  ... 
doi:10.1109/cvpr.2016.233 dblp:conf/cvpr/TangWGDGC16 fatcat:bbz6v5uyw5d6zbssua5ki6lbwi

Deep Domain Adaptive Object Detection: a Survey [article]

Wanyi Li, Fuyu Li, Yongkang Luo, Peng Wang, Jia sun
2020 arXiv   pre-print
Deep learning (DL) based object detection has achieved great progress.  ...  This paper aims to review the state-of-the-art progress on deep domain adaptive object detection approaches. Firstly, we introduce briefly the basic concepts of deep domain adaptation.  ...  adaptive representation learning paradigm for object detection.  ... 
arXiv:2002.06797v3 fatcat:mozths3lk5djndue6dzefxuq3q

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TIP 2021 5154-5167 Multi-Hierarchical Category Supervision for Weakly-Supervised Temporal Action Localization.  ...  ., +, TIP 2021 5920-5932 Modeling Sub-Actions for Weakly Supervised Temporal Action Localization.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Towards Single Stage Weakly Supervised Semantic Segmentation [article]

Peri Akiva, Kristin Dana
2021 arXiv   pre-print
The costly process of obtaining semantic segmentation labels has driven research towards weakly supervised semantic segmentation (WSSS) methods, using only image-level, point, or box labels.  ...  supervision.  ...  Very deep convo- Is object localization for free? - weakly-supervised learning lutional networks for large-scale image recognition. arXiv with convolutional neural networks.  ... 
arXiv:2106.10309v2 fatcat:l3oafc7rz5frbiynru2vn6ogfa

Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation [article]

Huiling Wang, Tapani Raiko, Lasse Lensu, Tinghuai Wang, Juha Karhunen
2016 arXiv   pre-print
However, for video semantic object segmentation, a domain where labels are scarce, effectively exploiting the representation power of CNN with limited training data remains a challenge.  ...  We propose a semi-supervised approach to adapting CNN image recognition model trained from labeled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic  ...  This data-driven object representation underpins a robust object segmentation method for weakly labelled natural videos.  ... 
arXiv:1606.02280v1 fatcat:c4ynsqjrkjc37pmknd2zp7dztu

W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection

Yongqiang Zhang, Yancheng Bai, Mingli Ding, Yongqiang Li, Bernard Ghanem
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Weakly-supervised object detection has attracted much attention lately, since it does not require bounding box annotations for training.  ...  Although significant progress has also been made, there is still a large gap in performance between weakly-supervised and fully-supervised object detection.  ...  [23] propose classification adaptation to fine-tune the network, so that it can collect class specific object proposals, and detection adaptation is used to optimize the representations for the target  ... 
doi:10.1109/cvpr.2018.00103 dblp:conf/cvpr/ZhangBDLG18 fatcat:hy262v5bgfh2fhpwqlzzizgw24

Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization [article]

Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng, Yi Niu, Shiliang Pu, Fei Wu, Futai Zou
2019 arXiv   pre-print
In this paper, we propose a novel weakly-supervised framework by adversarial learning of two modules for eliminating such demerits.  ...  Since the frame-level or segment-level annotations of untrimmed videos require amounts of labor expenditure, studies on the weakly-supervised action detection have been springing up.  ...  Weakly-Supervised Object Localization. Weakly supervised object localization methods locate target objects using convolutional classification networks.  ... 
arXiv:1908.02422v1 fatcat:stwylcwezngnrisxpulvlagnou

Cross-Domain Weakly-Supervised Object Detection Through Progressive Domain Adaptation

Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, Kiyoharu Aizawa
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
In this paper, we present a framework for a novel task, cross-domain weakly supervised object detection, which addresses this question.  ...  Starting from a fully supervised object detector, which is pre-trained on the source domain, we propose a two-step progressive domain adaptation technique by fine-tuning the detector on two types of artificially  ...  Furuta is supported by the Grants-in-Aid for Scientific Research (16J07267) from JSPS.  ... 
doi:10.1109/cvpr.2018.00525 dblp:conf/cvpr/InoueFYA18 fatcat:g66l47zivbgl7li3p373pe5jxa
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