2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-3.1
Paper Title HIERARCHICAL REGION PROPOSAL REFINEMENT NETWORK FOR WEAKLY SUPERVISED OBJECT DETECTION
Authors Ming Zhang, Shuaicheng Liu, Bing Zeng, University of Electronic Science and Technology of China, China
SessionMLR-APPL-IP-3: Machine learning for image processing 3
LocationArea F
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Weakly supervised object detection (WSOD) has attracted more attention because it only requires image-level annotations to indicate whether a certain class exists. Most WSOD methods utilize multiple instance learning (MIL) to train an object detector where an image is treated as a bag of candidate proposals. Unlike fully supervised object detection (FSOD) that uses the object-aware region proposal network (RPN) to generate effective candidate proposals, WSOD only utilizes region proposal methods (e.g., selective search or edge boxes) due to the lack of instance-level annotations (i.e., bounding boxes). However, the quality of proposals can influence the training of the detector. To solve this problem, we propose a hierarchical region proposal refinement network (HRPRN) to refine these proposals gradually. Specifically, our network contains multiple weakly supervised detectors that are trained stage by stage. In addition, we propose an instance regression refinement model to generate object-aware coordinate offsets to refine proposals at each stage. In order to demonstrate the effectiveness of our method, we conduct experiments on PASCAL VOC 2007 dataset that is the widely used benchmark. Compared with our baseline method, online instance classifier refinement (OICR), our method achieves 9\% and 5.6\% improvements in terms of mAP and CorLoc, respectively.