RGB-D Co-attention Network for Semantic Segmentation
| Autor: | Xu Yang, Hao Zhou, Hai Huang, Zhaoliang Wan, Lu Qi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Channel (digital image)
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications 02 engineering and technology Dimension (vector space) Feature (computer vision) Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Key (cryptography) Fuse (electrical) RGB color model 020201 artificial intelligence & image processing Computer vision Segmentation Artificial intelligence business |
| Zdroj: | Computer Vision – ACCV 2020 ISBN: 9783030695248 ACCV (1) |
| Popis: | Incorporating the depth (D) information for RGB images has proven the effectiveness and robustness in semantic segmentation. However, the fusion between them is still a challenge due to their meaning discrepancy, in which RGB represents the color but D depth information. In this paper, we propose a co-attention Network (CANet) to capture the fine-grained interplay between RGB’ and D’ features. The key part in our CANet is co-attention fusion part. It includes three modules. At first, the position and channel co-attention fusion modules adaptively fuse color and depth features in spatial and channel dimension. Finally, a final fusion module integrates the outputs of the two co-attention fusion modules for forming a more representative feature. Our extensive experiments validate the effectiveness of CANet in fusing RGB and D features, achieving the state-of-the-art performance on two challenging RGB-D semantic segmentation datasets, i.e., NYUDv2, SUN-RGBD. |
| Databáze: | OpenAIRE |
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