Instance-aware, Context-focused, and Memory-efficient
Weakly Supervised Object Detection

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


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 thumbnail


arXiv, 2020.


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



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.

    Thanks the SPADE folks for this beautiful webpage.