Asymmetric Non-Local Neural Networks for Semantic Segmentation
Huazhong University of Science and Technology · University of Oxford
Abstract
The non-local module works as a particularly useful technique for semantic segmentation while criticized for its prohibitive computation and GPU memory occupation. In this paper, we present Asymmetric Non-local Neural Network to semantic segmentation, which has two prominent components: Asymmetric Pyramid Non-local Block (APNB) and Asymmetric Fusion Non-local Block (AFNB). APNB leverages a pyramid sampling module into the non-local block to largely reduce the computation and memory consumption without sacrificing the performance. AFNB is adapted from APNB to fuse the features of different levels under a sufficient consideration of long range dependencies and thus considerably improves the performance.…
Citation impact
- FWCI
- 40.32
- Percentile
- 100%
- References
- 74
Authors
5Topics & keywords
- Computer science
- Segmentation
- Block (permutation group theory)
- Pyramid (geometry)
- Fuse (electrical)
- Computation
- Code (set theory)
- Set (abstract data type)
- Sustainable cities and communities