The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach
Open accessAbstract
We investigate a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems. Classification networks are only responsive to small and sparse discriminative regions from the object of interest, which deviates from the requirement of the segmentation task that needs to localize dense, interior and integral regions for pixel-wise inference. To mitigate this gap, we propose a new adversarial erasing approach for localizing and expanding object regions progressively. Starting with a single small object region, our proposed approach drives the classification network to sequentially discover new and complement object regions by erasing the current mined regions in an adversarial manner. These localized regions eventually constitute a dense and complete object region for learning semantic segmentation. To further enhance the quality of the discovered regions by adversarial erasing, an online prohibitive segmentation learning approach is developed to collaborate with adversarial erasing by providing auxiliary segmentation supervision modulated by the more reliable classification scores. Despite its apparent simplicity, the proposed approach achieves 55.0% and 55.7% mean Intersection-over-Union (mIoU) scores on PASCAL VOC 2012 val and test sets, which are the new state-of-the-arts.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213
Conditional Random Fields as Recurrent Neural Networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.179 · Cited by 2,386 · cited 1×
“Deep neural networks (DNNs) have achieved remarkable success on semantic segmentation tasks [2, 13, 15, 33], arguably benefiting from available resources of pixel-level annotated masks.”
BackgroundScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation
Lin, D., Dai, J., Jia, J., He, K., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.344 · Cited by 1,129
CNN: Single-label to Multi-label
Wei, Y., Xia, W., Huang, J., Ni, B., Dong, J., Zhao, Y., & Yan, S. (2014) · arXiv · doi:10.48550/arxiv.1406.5726 · Cited by 784
Self-citation
Research works printed but not linked: 29
- 10.1109/CVPR.2016.307 · DOI
- 10.1109/CVPR.2015.7299002 · DOI
- 10.1109/TPAMI.2015.2491929 · DOI · Paper page
- 10.1109/CVPR.2016.319 · DOI · Paper page
- kolesnikov, 2016, Seed, expand and constrain: Three principles for weakly-supervised image segmentation, ECCV, 695
- koltun, 2011, Efficient inference in fully connected crfs with gaussian edge potentials, NIPS
- 10.1109/CVPR.2015.7298748 · DOI
- 10.1109/TMM.2011.2174780 · doi:10.1109/tmm.2011.2174780
- 10.1109/CVPR.2015.7298965 · DOI · Paper page
- papandreou, 2015, Weakly-and semi-supervised learning of a dcnn for semantic image segmentation
- 10.1109/ICCV.2015.209 · DOI · Paper page
- pathak, 2014, Fully convolutional multi-class multiple instance learning
- 10.1109/CVPR.2015.7298780 · DOI · Paper page
- 10.1016/j.patcog.2016.01.015 · DOI
- 10.1109/ICCV.2011.6126299 · DOI
- 10.1109/ICCV.2015.191 · DOI · Paper page
- 10.1109/ICCV.2011.6126343 · DOI · Paper page
- wei, 2016, Stc: A simple to complex framework for weakly-supervised semantic segmentation, IEEE TPAMI
- 10.1145/2647868.2654889 · DOI · Paper page
- 10.1109/CVPR.2016.349 · DOI
- 10.1109/CVPR.2013.271 · DOI · Paper page
- 10.1109/CVPR.2014.49 · DOI · Paper page
- qi, 2016, Augmented feedback in semantic segmentation under image level supervision, ECCV, 90
- russakovsky, 2016, Whats the point: Semantic segmentation with point supervision, ECCV, 549
- rother, 2004, Grabcut: Interactive foreground extraction using iterated graph cuts, ACM Transactions on Graphics, 23, 309, 10.1145/1015706.1015720 · DOI · Paper page
- shimoda, 2016, Distinct class-specific saliency maps for weakly supervised semantic segmentation, EC-CV, 218
- saleh, 2016, Built-in foreground/background prior for weakly-supervised semantic segmentation, EC-CV, 413
- 10.1007/s11263-013-0620-5 · DOI · Paper page
- simonyan, 2015, Very deep convolutional networks for large-scale image recognition, International Conference on Learning Representations
Cited by
Random Erasing Data Augmentation
Zhong, Z., Zheng, L., Kang, G., Li, S., & Yang, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.7000 · Cited by 3,617
Structure-Measure: A New Way to Evaluate Foreground Maps
Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., & Borji, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.487 · Cited by 2,092
Self-citationObject-Contextual Representations for Semantic Segmentation
Yuan, Y., Chen, X., & Wang, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58539-6_11 · Cited by 1,285
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2996645 · Cited by 1,259
Singh, K. K., & Lee, Y. J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.381 · Cited by 724
Causal Intervention for Weakly-Supervised Semantic Segmentation
Zhang, D., Zhang, H., Tang, J., Hua, X., & Sun, Q. (2020) · arXiv · doi:10.48550/arxiv.2009.12547 · Cited by 353
Representative Forgery Mining for Fake Face Detection
Wang, C., & Deng, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01468 · Cited by 288
Weakly- and Semi-supervised Panoptic Segmentation
Li, Q., Arnab, A., & Torr, P. H. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01267-0_7 · Cited by 171
Interactive Object Segmentation With Inside-Outside Guidance
Zhang, S., Liew, J. H., Wei, Y., Wei, S., & Zhao, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01225 · Cited by 142
Self-citationCORE: Consistent Representation Learning for Face Forgery Detection
Ni, Y., Meng, D., Yu, C., Quan, C., Ren, D., & Zhao, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00011 · Cited by 136
Self-citationSynthesizing Supervision for Learning Deep Saliency Network without Human Annotation
Zhang, D., Han, J., Zhang, Y., & Xu, D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2900649 · Cited by 118
Large-Scale Unsupervised Semantic Segmentation
Gao, S., Li, Z.-Y., Yang, M.-H., Cheng, M.-M., Han, J., & Torr, P. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3218275 · Cited by 88
Self-citationMohamed, E., Sirlantzis, K., & Howells, G. (2022) · Displays · doi:10.1016/j.displa.2022.102239 · Cited by 67
Metadata
- Authors
- Wei, Y., Feng, J., Liang, X., Cheng, M.-M., Zhao, Y., & Yan, S.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 6488-6496
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2600144439
- Cited by
- 863
- References
- 6
- FWCI
- 22.57
- Type
- conference paper
- Language
- en
- Open access
- green
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 ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
