2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDIVMSP-29.3
Paper Title CGAN-NET: CLASS-GUIDED ASYMMETRIC NON-LOCAL NETWORK FOR REAL-TIME SEMANTIC SEGMENTATION
Authors Hanlin Chen, National University of Defense Technology, China; Qingyong Hu, University of Oxford, United Kingdom; Jungang Yang, Jing Wu, National University of Defense Technology, China; Yulan Guo, National University of Defense Technology, Sun Yat-sen University, China
SessionIVMSP-29: Semantic Segmentation
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract By introducing various non-local blocks to capture the long-range dependencies, remarkable progress has been achieved in semantic segmentation recently. However, the improvement in segmentation accuracy usually comes at the price of significant reductions in network efficiency, as non-local block usually requires expensive computation and memory for dense pixel-to-pixel correlation. In this paper, we introduce a Class Guided Asymmetric Non-local Network (CGAN-Net) to enhance the class-discriminability in learned feature map, while maintaining real-time efficiency. The key to our approach is to calculate the dense similarity matrix in coarse semantic prediction maps, instead of the high-dimensional latent feature map. This is not only computationally and memory efficient, but helps to learn query-dependent global context. Experiments conducted on Cityscape and CamVid demonstrate the compelling performance of our CGAN-Net. In particular, our network achieves 76.8% mean IoU on the Cityscapes test set with a speed of 38 FPS for 1024x2048 images on a single Tesla V100 GPU.