Class-Aware Contextual Information for Semantic Segmentation
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, 2023-June, pp. 1-5
- Issue Date:
- 2023-05-05
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Class-Aware_Contextual_Information_for_Semantic_Segmentation.pdf | Published version | 3.83 MB |
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Exploring spatial contextual information is a well adopted approach to achieving better semantic segmentation performance However most existing methods neglect the class association between the neighboring pixels In this paper we propose a CACINet which consists of a Semantic Affinity Module SAM and a Class Association Module CAM to generate class aware contextual information among pixels on a fine grained level SAM analyzes the affiliation of any two given pixels belonging to the same or different class It produces intra class and inter class pixel contextual information CAM classifies the image into different class regions globally and then it encodes the pixel based on the degree of affiliation of the pixels with each class in the image In this way it augments the class affiliation of the pixels into the corresponding context calculation Comprehensive experiments demonstrate that the proposed method achieves competitive performance on two semantic segmentation benchmarks ADE20K and PASCAL Context
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