Skip to main content
SpringerLink
Log in

Cascade Attentive Dropout for Weakly Supervised Object Detection

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Weakly supervised object detection (WSOD) aims to classify and locate objects with only image-level supervision. Many WSOD approaches adopt multiple instance learning as the initial model, which is prone to converge to the most discriminative object regions while ignoring the whole object, and therefore reduce the model detection performance. In this paper, a novel cascade attentive dropout strategy is proposed to alleviate the part domination problem, together with an improved global context module. We purposely discard attentive elements in both channel and space dimensions, and capture the inter-pixel and inter-channel dependencies to induce the model to better understand the global context. Extensive experiments have been conducted on the challenging PASCAL VOC 2007 benchmarks, which achieve 49.8% mAP and 66.0% CorLoc, outperforming state-of-the-arts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. More details about the issue can refer to https://github.com/ppengtang/pcl.pytorch/issues/9.

References

  1. Kantorov V, Oquab M, Cho M, Laptev I (2016) Contextlocnet: context-aware deep network models for weakly supervised localization. In: European Conference on computer vision. Springer, pp 350–365

  2. Wei Y, Shen Z, Cheng B, Shi H, Xiong J, Feng J, Huang T (2018) Ts2c: tight box mining with surrounding segmentation context for weakly supervised object detection. In: Proceedings of the European conference on computer vision (ECCV), pp 434–450

  3. Tang P, Wang X, Bai S, Shen W, Bai X, Liu W, Yuille A (2018) Pcl: proposal cluster learning for weakly supervised object detection. IEEE Trans Pattern Anal Mach Intell 42(1):176–191

    Article  Google Scholar 

  4. Tang P, Wang X, Bai X, Liu W (2017) Multiple instance detection network with online instance classifier refinement. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2843–2851

  5. Wan F, Liu C, Ke W, Ji X, Jiao J, Ye Q (2019) C-mil: continuation multiple instance learning for weakly supervised object detection. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 2199–2208

  6. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

  7. Yu J, Lin Z, Yang J, Shen X, Lu X, Huang TS (2018) Generative image inpainting with contextual attention. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5505–5514

  8. Sinha A, Dolz J (2019) Multi-scale guided attention for medical image segmentation. arXiv preprint arXiv:1906.02849

  9. Yu J, Zhu C, Zhang J, Huang Q, Tao D (2019) Spatial pyramid-enhanced NetVLAD with weighted triplet loss for place recognition. IEEE Trans Neural Netw Learn Syst 31(2):661–674

    Article  Google Scholar 

  10. Bilen H, Vedaldi A (2016) Weakly supervised deep detection networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2846–2854

  11. Zhang Y, Bai Y, Ding M, Li Y, Ghanem B (2018) W2f: a weakly-supervised to fully-supervised framework for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 928–936

  12. Wang J, Yao J, Zhang Y, Zhang R (2018) Collaborative learning for weakly supervised object detection. arXiv preprint arXiv:1802.03531

  13. Arun A, Jawahar C, Kumar MP (2019) Dissimilarity coefficient based weakly supervised object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9432–9441

  14. Gao Y, Liu B, Guo N, Ye X, Wan F, You H, Fan D (2019) C-midn: coupled multiple instance detection network with segmentation guidance for weakly supervised object detection. In: Proceedings of the IEEE international conference on computer vision, pp 9834–9843

  15. Zeng Z, Liu B, Fu J, Chao H, Zhang L (2019) Wsod2: learning bottom-up and top-down objectness distillation for weakly-supervised object detection. In: Proceedings of the IEEE international conference on computer vision, pp 8292–8300

  16. Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580

  17. Wan L, Zeiler M, Zhang S, Le Cun Y, Fergus R (2013) Regularization of neural networks using dropconnect. In: International conference on machine learning, pp 1058–1066

  18. Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: International conference on machine learning, pp 1050–1059

  19. DeVries T, Taylor GW (2017) Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552

  20. Tompson J, Goroshin R, Jain A, LeCun Y, Bregler C (2015) Efficient object localization using convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 648–656

  21. Choe J, Shim H (2019) Attention-based dropout layer for weakly supervised object localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2219–2228

  22. Li X, Wang W, Hu X, Yang J (2019) Selective kernel networks

  23. Woo S, Park J, Lee JY, So Kweon I (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19

  24. Uijlings JR, Van De Sande KE, Gevers T, Smeulders AW (2013) Selective search for object recognition. Int J Comput Vis 104(2):154–171

    Article  Google Scholar 

  25. Wan F, Wei P, Jiao J, Han Z, Ye Q (2018) Min-entropy latent model for weakly supervised object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1297–1306

  26. Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794–7803

  27. Felipe Zeni L, Jung CR (2020) Distilling knowledge from refinement in multiple instance detection networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 768–769

  28. Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88(2):303–338

    Article  Google Scholar 

  29. Deselaers T, Alexe B, Ferrari V (2012) Weakly supervised localization and learning with generic knowledge. Int J Comput Vis 100(3):275–293

    Article  MathSciNet  Google Scholar 

  30. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  31. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248–255

  32. Jie Z, Wei Y, Jin X, Feng J, Liu W (2017) Deep self-taught learning for weakly supervised object localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1377–1385

  33. Diba A, Sharma V, Pazandeh A, Pirsiavash H, Van Gool L (2017) Weakly supervised cascaded convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 914–922

  34. Gao M, Li A, Yu R, Morariu VI, Davis LS (2018) C-wsl: count-guided weakly supervised localization. In: Proceedings of the European conference on computer vision (ECCV), pp 152–168

  35. Tang P, Wang X, Wang A, Yan Y, Liu W, Huang J, Yuille A (2018) Weakly supervised region proposal network and object detection. In: Proceedings of the European conference on computer vision (ECCV), pp 352–368

  36. Zhang X, Feng J, Xiong H, Tian Q (2018) Zigzag learning for weakly supervised object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4262–4270

  37. Li X, Kan M, Shan S, Chen, X (2019) Weakly supervised object detection with segmentation collaboration. In: Proceedings of the IEEE international conference on computer vision, pp 9735–9744

  38. Yang K, Li D, Dou Y (2019) Towards precise end-to-end weakly supervised object detection network. In: Proceedings of the IEEE international conference on computer vision, pp 8372–8381

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 61573168)

Author information

Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214100, China

    Wenlong Gao, Ying Chen & Yong Peng

Authors
  1. Wenlong Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ying Chen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, W., Chen, Y. & Peng, Y. Cascade Attentive Dropout for Weakly Supervised Object Detection. Neural Process Lett 55, 6907–6923 (2023). https://doi.org/10.1007/s11063-023-11243-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-023-11243-y

Keywords

Search

Navigation