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

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDIMT-1.6
Paper Title ReinforceDet: Object Detection by Integrating Reinforcement Learning with Decoupled Pipeline
Authors Man Zhou, University of Science and Technology of China, China; Liu Liu, Shanghai Jiao Tong University, China; Rujing Wang, Chinese Academy of Sciences, China
SessionIMT-1: Computational Imaging Learning-based Models
LocationArea J
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Computational Imaging Methods and Models: Learning-Based Models
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recent object detection methods largely rely on numerous pre-defined anchors that suffer from huge computational cost and resource consumption. To solve this issue, we propose a low-memory deep reinforcement learning based anchor-free object detection approach, namely ReinforceDet, which computes few but accurate region proposals for detection. Specifically, the extracted feature maps are fed into a reinforcement learning network to localize objects as initial region proposals with our re-designed reward function and then adopt another neural network to refine them. To speed up this process in test phase, we decouple the two-branch CNN networks as light-head cascaded subnetworks, named IoU-net and bounding box net. Experimental results show that ReinforceDet could obtain the state-of-the-art performance with much lower compitational and memory cost.