Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection (original) (raw)
Zhongzheng Ren Zhiding Yu Xiaodong Yang Ming-Yu Liu
Yong Jae Lee Alexander G. Schwing Jan Kautz
UIUC NVIDIA UC Davis
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020
Paper |Code | Slides |Bibtex
Abstract
Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training. However, major challenges remain: (1) differentiation of object instances can be ambiguous; (2) detectors tend to focus on discriminative parts rather than entire objects; (3) without ground truth, object proposals have to be redundant for high recalls, causing significant memory consumption. Addressing these challenges is difficult, as it often requires to eliminate uncertainties and trivial solutions. To target these issues we develop an instance-aware and context-focused unified framework. It employs an instance-aware self-training algorithm and a learnable Concrete DropBlock while devising a memory-efficient sequential batch back-propagation. Our proposed method achieves state-of-the-art results on COCO (12.1% AP, 24.8% AP50), VOC 2007 (54.9% AP), and VOC 2012 (52.1% AP), improving baselines by great margins. In addition, the proposed method is the first to benchmark ResNet based models and weakly supervised video object detection..
Paper
arXiv, 2020.
Citation
Z. Ren, Z. Yu, X. Yang, M.-Y. Liu, Y. J. Lee, A. G. Schwing, J. Kautz.
Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection.
CVPR 2020.Bibtex
Code
CVPR 1-min video
Related Work
- H. Bilen and A. Vedaldi. "Weakly supervised deep detection networks", in CVPR 2016.
- V. Kantorov, M. Oquab, M. Cho, and I. Laptev. " Contextlocnet: Context-aware deep network models for weakly supervised localization.", in ECCV 2016.
- P. Tang, X. Wang, X. Bai, W. Liu. "Multiple Instance Detection Network with Online Instance Classifier Refinement", in CVPR 2017.
- Y. Gao, B. Liu, N. Guo, X. Ye, F. Wan, H. You, and D. Fan "C-MIDN: Coupled multiple instance detection network with segmentation guidance for weakly supervised object detection", in ICCV 2019.
Acknowledgement
Zhongzheng Ren is supported by Yunni & Maxine Pao Memorial Fellowship. This work is supported in part by NSF under Grant No. 1718221 and No. 1751206. Zhongzheng Ren and Xiaodong Yang contributed to the work while at NVIDIA.
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