Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-Supervised Object and Action Localization
Open accessAbstract
We propose `Hide-and-Seek', a weakly-supervised framework that aims to improve object localization in images and action localization in videos. Most existing weakly-supervised methods localize only the most discriminative parts of an object rather than all relevant parts, which leads to suboptimal performance. Our key idea is to hide patches in a training image randomly, forcing the network to seek other relevant parts when the most discriminative part is hidden. Our approach only needs to modify the input image and can work with any network designed for object localization. During testing, we do not need to hide any patches. Our Hide-and-Seek approach obtains superior performance compared to previous methods for weakly-supervised object localization on the ILSVRC dataset. We also demonstrate that our framework can be easily extended to weakly-supervised action localization.
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Various types of industrial image defects do not meet all the assumptions of semantic relevance and geometric correlation.
“However, various types of industrial image defects do not meet all the above assumptions [42,43].”
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“…51.46 Backprop on GoogLeNet [41] 61.31 50.55 AlexNet-GAP [1] 67.19 52.16 VGGnet-GAP [1] 57.20 45.14 GoogLeNet-GAP [1] 56.40 43.00 GoogLeNet-HaS-32 [2] 54.53 - VGGnet-ACoL [20] 54.17 40.57 GoogLeNet-ACoL [20] 53 baseline methods on the ILSVRC 2016 dataset, as it has more than 1.2 million images of…”
MethodInfluentialLeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis
Huu, Q., Uga, H., Kagiwada, S., & Iyatomi, H. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.3041499 · Cited by 247 · see citing works
Instance-Aware, Context-Focused, and Memory-Efficient Weakly Supervised Object Detection
Ren, Z., Yu, Z., Yang, X., Liu, M.-Y., Lee, Y. J., Schwing, A. G., & Kautz, J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01061 · Cited by 230 · see citing works
Self-citationTS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization
Gao, W., Wan, F., Pan, X., Peng, Z., Tian, Q. C., Han, Z., Zhou, B., & Ye, Q. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00288 · Cited by 236 · see citing works
“tion [30, 43, 45] or adversarial training [7, 20, 40, 47, 45, 30].”
MethodDANet: Divergent Activation for Weakly Supervised Object Localization
Xue, H., Liu, C., Wan, F., Jiao, J., Ji, X., & Ye, Q. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00669 · Cited by 222 · see citing works
“propagation [40], data argumentation [13], dilated convolution [31], and adversarial erasing [37, 12] to expand class activation maps and pursue full object extent.”
BackgroundHu, T., Qi, H., Huang, Q., & Lu, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.09891 · Cited by 252 · see citing works
“Before our work, random spatial image augmentation methods [19, 3, 24], such as image cropping and image dropping, have been proposed and proved to be effective in improving the robustness of deep models.”
BackgroundMethodLearning a Mixture of Granularity-Specific Experts for Fine-Grained Categorization
Zhang, L., Huang, S., Liu, W., & Tao, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00842 · Cited by 214 · see citing works
“Given attention map we construct input for next expert using technique similar to weakly supervised object localization [46, 22, 29].”
MethodRegional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation
Zhou, T., Zhang, M., Zhao, F., & Li, J. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00426 · Cited by 215 · see citing works
“…more complete object regions by, e.g ., region growing to expand initial responses [24, 30, 60], adversarial erasing in a hide-and-seek fashion [23, 32, 33, 64], feature enrichment to collect within-image contexts [66, 72], seeking auxiliary saliency supervisions [35, 71, 74], or self-supervised…”
BackgroundKernelized Memory Network for Video Object Segmentation
Seong, H., Hyun, J., & Kim, E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58542-6_38 · Cited by 204 · see citing works
Evaluating Weakly Supervised Object Localization Methods Right
Choe, J., Oh, S. J., Lee, S., Chun, S., Akata, Z., & Shim, H. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00320 · Cited by 204 · see citing works
“WSOL techniques since CAM have focused on this limitation and have proposed different architectural [57, 58, 6] and dataaugmentation [26, 55] solutions.”
BackgroundMethodInfluentialImproving Referring Expression Grounding With Cross-Modal Attention-Guided Erasing
Liu, X., Wang, Z., Shao, J., Wang, X., & Li, H. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00205 · Cited by 202 · see citing works
“Previous works has explored erasing image regions for object detection [29], person re-identification [10], weakly supervised detection [25, 7] and semantic segmentation [30].”
BackgroundMethodMin-Entropy Latent Model for Weakly Supervised Object Detection
Wan, F., Wei, P., Jiao, J., Han, Z., & Ye, Q. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00141 · Cited by 200 · see citing works
Weakly-Supervised Action Localization by Generative Attention Modeling
Shi, B., Dai, Q., Mu, Y., & Wang, J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00109 · Cited by 187 · see citing works
“Hide-and-Seek [44] Weak - 36.”
Weakly-Supervised Action Localization With Background Modeling
Nguyen, P., Ramanan, D., & Fowlkes, C. C. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00560 · Cited by 182 · see citing works
3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization
Narayan, S., Cholakkal, H., Khan, F. S., & Shao, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00877 · Cited by 179 · see citing works
“The work of [25, 14, 28] use action category labels in videos for temporal localization, whereas [13] uses point-level supervision to spatio-temporally localize the actions.”
BackgroundWeakly-Supervised Video Moment Retrieval via Semantic Completion Network
Lin, Z., Zhao, Z., Zhang, Z., Wang, Q., & Liu, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6820 · Cited by 172 · see citing works
CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning
Zhang, C., Cao, M., Yang, D., Chen, J., & Zou, Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01575 · Cited by 170 · see citing works
“Hide-and-seek [36] and MAAN [43] try to extend the discriminative regions via randomly hiding patches or suppressing the dominant response, respectively.”
BackgroundOn Feature Normalization and Data Augmentation
Li, B., Wu, F., Lim, S.-N., Belongie, S., & Weinberger, K. Q. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01220 · Cited by 168 · see citing works
“Many subsequent papers proposed alternative flavors of this augmentation approach based on similar insights [4, 5, 7, 27, 28, 36, 51, 63, 70].”
BackgroundMethodUnified Multisensory Perception: Weakly-Supervised Audio-Visual Video Parsing
Tian, Y., Li, D., & Xu, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_26 · Cited by 159 · see citing works
Liu, Y., Wu, Y.-H., Wen, P., Shi, Y., Qiu, Y., & Cheng, M. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3023152 · Cited by 160 · see citing works
“These methods either adopt image hiding and erasure to prevent a classifier from focusing exclusively on the discriminative parts of objects [23], [31], [49], or expand the CAM using feature-level processing [27], [29], [50]–[53] and region growing [30], [32], [54], [55] techniques.”
Two-Stream Consensus Network for Weakly-Supervised Temporal Action Localization
Zhai, Y., Wang, L., Tang, W., Zhang, Q., Yuan, J., & Hua, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58539-6_3 · Cited by 156 · see citing works
Complementary Patch for Weakly Supervised Semantic Segmentation
Zhang, F., Gu, C., Zhang, C., & Dai, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00715 · Cited by 159 · see citing works
Temporal Complementary Learning for Video Person Re-identification
Hou, R., Chang, H., Ma, B., Shan, S., & Chen, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58595-2_24 · Cited by 155 · see citing works
Masked Discrimination for Self-supervised Learning on Point Clouds
Liu, H., Cai, M., & Lee, Y. J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20086-1_38 · Cited by 156 · see citing works
Self-citationFine-Grained Recognition: Accounting for Subtle Differences between Similar Classes
Sun, G., Cholakkal, H., Khan, S. H., Khan, F. S., & Shao, L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6882 · Cited by 152 · see citing works
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut
Wang, Y., Shen, X., Hu, S. X., Yuan, Y., Crowley, J. L., & Vaufreydaz, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01414 · Cited by 155 · see citing works
“The methods can be divided to two groups: models initialized with ImageNet-1K supervised pre-training [3, 13, 22, 46, 71, 76, 77] and models inilialized with ImageNet-1K self-supervised pre-training [45].”
SF-Net: Single-Frame Supervision for Temporal Action Localization
Ma, F., Zhu, L., Yang, Y., Zha, S., Kundu, G., Feiszli, M., & Shou, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58548-8_25 · Cited by 152 · see citing works
Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos
Nwoye, C. I., Mutter, D., Marescaux, J., & Padoy, N. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-01958-6 · Cited by 149 · see citing works
“According to [20], this enables the network to learn meticulously the necessary details of the object of interest.”
BackgroundMethodInfluentialXie, J., Xiang, J., Chen, J., Hou, X., Zhao, X., & Shen, L. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00106 · Cited by 155 · see citing works
“To solve the above problem, various methods [6, 24, 31, 42–44, 48, 49] have been proposed.”
A survey of semi- and weakly supervised semantic segmentation of images
Zhang, M., Zhou, Y. Z., Zhao, J., Man, Y., Liu, B., & Yao, R. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09792-7 · Cited by 144 · see citing works
“The idea of hide-and-seek is also applied by Singh and Lee (2017).”
BackgroundWeakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning
Luo, Z., Guillory, D., Shi, B., Ke, W., Wan, F., Darrell, T., & Xu, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58526-6_43 · Cited by 140 · see citing works
High-Quality Proposals for Weakly Supervised Object Detection
Cheng, G., Yang, J., Gao, D., Guo, L., & Han, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2987161 · Cited by 139 · see citing works
“More recently, WSOD has attracted considerable attention because of its broad applications [1]–[12].”
BackgroundActivation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation
Qin, J., Wu, J., Xiao, X., Li, L., & Wang, X. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20108 · Cited by 137 · see citing works
TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks
Alwassel, H., Giancola, S., & Ghanem, B. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00356 · Cited by 136 · see citing works
“Video understanding is thriving in the computer vision community, and it manifests in several challenging tasks such as action classification [13, 34, 41, 69], activity localization [16, 36, 86], and video captioning [25, 53, 71, 88].”
BackgroundA New Dataset, Poisson GAN and AquaNet for Underwater Object Grabbing
Liu, C., Wang, Z., Wang, S., Tang, T., Tao, Y., Yang, C., Li, H., Liu, X., & Fan, X. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3100059 · Cited by 137 · see citing works
“Among all the methods, information-dropping methods ( i.e. , GridMask, RErase, Cutout, HaS) perform worse than than the baseline method because dropping information could make the class-imbalance problem more serious.”
A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization
Islam, A., Long, C., & Radke, R. J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16256 · Cited by 17 · see citing works
Weakly Supervised Temporal Action Localization Through Contrast Based Evaluation Networks
Liu, Z., Wang, L., Zhang, Q., Gao, Z., Niu, Z., Zheng, N., & Hua, G. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00400 · Cited by 122 · see citing works
“W ea k ly -s u p er v is ed Hide-and-Seek [32] 19.”
BackgroundMethodInfluentialMai, J., Yang, M., & Luo, W. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00879 · Cited by 125 · see citing works
“Recently, various methods [41, 43, 28, 13, 40, 39, 2] have been developed to handle this challenging task.”
BackgroundMethodInfluentialSaliencyMix: A Saliency Guided Data Augmentation Strategy for Better RegularizationPreprint
Uddin, A. F. M. S., Monira, M. S., Shin, W., Chung, T., & Bae, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.01791 · Cited by 152 · see citing works
“Dropout (Nitish et al., 2014; Tompson et al., 2015) and regional dropout (Junsuk & Hyunjung, 2019; Terrance & Graham, 2017; Golnaz et al., 2018; Singh & Lee, 2017; Zhun et al., 2017) are two established training strategies where the former randomly turns off some internal activations and later…”
Weakly-supervised Temporal Action Localization by Uncertainty Modeling
Lee, P., Wang, J., Lu, Y., & Byun, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i3.16280 · Cited by 22 · see citing works
“(Nguyen et al. 2018) propose a sparsity regularization, while (Singh and Lee 2017) and (Yuan et al. 2019) extend small discriminative parts.”
BackgroundRethinking the Route Towards Weakly Supervised Object Localization
Zhang, C.-L., Cao, Y.-H., & Wu, J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01347 · Cited by 122 · see citing works
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection
Liu, Y., Sheng, L., Shao, J., Yan, J., Xiang, S., & Pan, C. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240567 · Cited by 119 · see citing works
“Recently, many researches on WSD [2, 8, 17, 26, 46] have been conducted.”
BackgroundRethinking Class Activation Mapping for Weakly Supervised Object Localization
Bae, W., Noh, J., & Kim, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_37 · Cited by 118 · see citing works
Li, J., Li, W., Sisk, A. E. J., Ye, H., Wallace, W. D., Speier, W. F., & Arnold, C. W. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104253 · Cited by 118 · see citing works
ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization
He, B., Yang, X., Kang, L., Cheng, Z., Zhou, X., & Shrivastava, A. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01355 · Cited by 119 · see citing works
Choe, J., Lee, S., & Shim, H. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2999099 · Cited by 114 · see citing works
What makes instance discrimination good for transfer learning?
Zhao, N., Wu, Z., Lau, R. W. H., & Lin, S. (2020) · arXiv · doi:10.48550/arxiv.2006.06606 · Cited by 115 · see citing works
Inter-Image Communication for Weakly Supervised Localization
Zhang, X., Wei, Y., & Yang, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58529-7_17 · Cited by 112 · see citing works
Survey: Image mixing and deleting for data augmentation
Naveed, H., Anwar, S., Hayat, M., Javed, K., & Mian, A. S. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107791 · Cited by 126 · see citing works
Group-Wise Learning for Weakly Supervised Semantic Segmentation
Zhou, T., Li, L., Li, X., Feng, C.-M., Li, J., & Shao, L. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3132834 · Cited by 108 · see citing works
Uncertainty Guided Collaborative Training for Weakly Supervised Temporal Action Detection
Yang, W., Zhang, T., Yu, X., Tian, Q. C., Zhang, Y., & FengWu, F. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00012 · Cited by 110 · see citing works
“And existing works can be mainly categorized into three groups, learning background suppression attention weights [31, 32, 36, 21, 53], learning discriminative features [35, 30] and erasing discriminative segments during training [40, 56, 54].”
BackgroundTN-ZSTAD: Transferable Network for Zero-Shot Temporal Activity Detection
Zhang, L., Chang, X., Liu, J., Luo, M., Li, Z., Yao, L., & Hauptmann, A. G. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183586 · Cited by 106 · see citing works
“Singh et al. [47] proposed the Hide and Seek technique, which randomly hides regions to force the model to focus on multiple relevant parts of an activity, and then thresholds class activation maps to obtain the activity’s start-end times.”
BackgroundSiddiquee, M. M. R., Zhou, Z., Tajbakhsh, N., Feng, R., Gotway, M. B., Bengio, Y., & Liang, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00028 · Cited by 105 · see citing works
“Pinheiro and Collobert [23] replaced the original GAP with a log-sum-exponential pooling layer, while other works [28, 36] aim to force the CAM to discover the complementary parts rather than just the most discriminative parts of the objects.”
BackgroundMethodShallow Feature Matters for Weakly Supervised Object Localization
Wei, J., Wang, Q., Li, Z., Wang, S., Zhou, S., & Cui, S. R. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00593 · Cited by 104 · see citing works
González-Gonzalo, C., Thee, E. F., Klaver, C. C. W., Lee, A. Y., Schlingemann, R. O., Tufail, A., Verbraak, F., & Sánchez, C. I. (2022) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2021.101034 · Cited by 103 · see citing works
“Firstly, attribution techniques were developed and optimized with natural images, and it has been shown that they localize only the most discriminative regions (Singh and Lee, 2017).”
Deep Anatomical Context Feature Learning for Cephalometric Landmark Detection
Oh, K., Oh, I.-S., Le, V. N. T., & Lee, D. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3002582 · Cited by 105 · see citing works
“The studies [29]–[31] for the weakly supervised object localization presented a drawback of CNNs that tends to focus on the most discriminative part, and proposed a novel paradigm that forces networks to seek other relevant parts by hiding important image patches while training.”
BackgroundLooking Beyond Single Images for Weakly Supervised Semantic Segmentation Learning
Wang, W., Sun, G., & Gool, L. V. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3168530 · Cited by 100 · see citing works
“The third class drives the classification network to focus on different parts of objects, through hiding/erasing some high response regions [17], [18], [19], [37].”
BackgroundAction Unit Memory Network for Weakly Supervised Temporal Action Localization
Luo, W., Zhang, T., Yang, W., Liu, J., Mei, T., Wu, F., & Zhang, Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00984 · Cited by 99 · see citing works
“To relieve this problem, the weakly supervised setting that only requires video-level category labels is proposed [37, 55, 39, 37, 55, 53, 29, 30, 45, 46].”
Kang, S., Hu, Z., Liu, L., Zhang, K., & Cao, Z. (2025) · Electronics · doi:10.3390/electronics14061104 · Cited by 98 · see citing works
Zeng, R., Gan, C., Chen, P., Huang, W., Wu, Q., & Tan, M. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2922108 · Cited by 96 · see citing works
EPIC-KITCHENS-100Preprint
Damen, D., Doughty, H., Farinella, G. M., Furnari, A., Kazakos, E., Ma, J., Moltisanti, D., Munro, J., Perrett, T., Price, W., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.13256 · Cited by 111 · see citing works
“Previous weakly-supervised approaches utilised video-level or transcript supervision, where the set [64,65,66,67,68,69] or sequence [70,71,72,73,74,75] of actions in the video are used in training, without temporal bounds.”
BackgroundC-WSL: Count-Guided Weakly Supervised Localization
Gao, M., Li, A., Yu, R., Morariu, V. I., & Davis, L. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_10 · Cited by 96 · see citing works
A Comprehensive Survey on Data Augmentation
Wang, Z., Wang, P., Liu, K., Wang, P., Fu, Y., Lu, C.-T., Aggarwal, C. C., Pei, J., & Zhou, Y. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3622600 · Cited by 161 · see citing works
“and [68] focus on improving robustness with the help of STL components.”
Temporal Structure Mining for Weakly Supervised Action Detection
Yu, T., Ren, Z., Li, Y., Yan, E., Xu, N., & Yuan, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00562 · Cited by 91 · see citing works
Exploring Heterogeneous Clues for Weakly-Supervised Audio-Visual Video Parsing
Wu, Y., & Yang, Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00138 · Cited by 95 · see citing works
“There are also weakly-supervised works [24, 26, 29, 36, 44] proposed for action localization.”
BackgroundForeground-Action Consistency Network for Weakly Supervised Temporal Action Localization
Huang, L., Wang, L., & Li, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00790 · Cited by 94 · see citing works
“1, the attention mechanism (either class-agnostic attention or class-wise attention) usually suffers from the discrepancy [36, 49, 19] between classification and detection, i.e ., the attention scores being concentrated around most discrim-a inative action snippets or wrongly focus on background…”
Step-by-step Erasion, One-by-one Collection
Zhong, J.-X., Li, N., Kong, W., Zhang, T., Li, T. H., & Li, G. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240511 · Cited by 103 · see citing works
“ansfer between video frames and web images for fine-grained action localization. Wang etal.[48] establish the UntrimmedNets to work on weakly supervised action detection problem. The work proposed in [39] shares a similar training strategy with our approach, and the difference is that it trains a single classifier by…”
Fine-grained Temporal Contrastive Learning for Weakly-supervised Temporal Action Localization
Gao, J., Chen, M., & Xu, C. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01937 · Cited by 95 · see citing works
“Currently, most existing approaches can be roughly divided into three groups, namely attention-based methods [26, 26, 39, 42, 49, 56, 57, 68], MIL-based methods [32, 43, 45, 48, 54], and erasing-based methods [62, 72, 74].”
BackgroundUnveiling the Potential of Structure Preserving for Weakly Supervised Object Localization
Pan, X., Gao, Y., Lin, Z., Tang, F., Dong, W., Yuan, H., Huang, F., & Xu, C. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01147 · Cited by 95 · see citing works
Mix-ViT: Mixing attentive vision transformer for ultra-fine-grained visual categorization
Yu, X., Wang, J., Zhao, Y. S., & Gao, Y. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109131 · Cited by 92 · see citing works
Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection
Xu, Y., Zhang, C., Cheng, Z., Xie, J., Niu, Y. F., Pu, S., & Wu, F. Q. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019070 · Cited by 89 · see citing works
Combinational Class Activation Maps for Weakly Supervised Object Localization
Yang, S., Kim, Y., Kim, Y., & Kim, C. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093566 · Cited by 90 · see citing works
Wan, X., Zhang, X., & Liu, L. (2021) · Applied Sciences · doi:10.3390/app11062606 · Cited by 90 · see citing works
“However, various types of industrial image defects do not meet all the above assumptions [42,43].”
BackgroundGeometry Constrained Weakly Supervised Object Localization
Lu, W., Jia, X., Xie, W., Shen, L., Zhou, Y., & Duan, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58574-7_29 · Cited by 89 · see citing works
- Authors
- Singh, K. K., & Lee, Y. J.
- Affiliations
- Intel (United States) (US)
- University of California, Davis (US)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 3544-3553
- OpenAlex
- W2606520630
- Cited by
- 724
- References
- 57
- Citations by year
- 2026: 19 · 2025: 49 · 2024: 74 · 2023: 121 · 2022: 129 · 2021: 137 · 2020: 98 · 2019: 70 · 2018: 25 · 2017: 2
- FWCI
- 15.59
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nvidia
- Amazon Web Services
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- weakly supervised object localization, data augmentation, image classification, video action recognition
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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