Learning transferable image-level features for zero-shot semantic segmentation

Yanji Hao

doi:10.1117/12.3040756

10 September 2024 Learning transferable image-level features for zero-shot semantic segmentation

Yanji Hao

Author Affiliations +

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 132570H (2024) https://doi.org/10.1117/12.3040756
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

The current semantic segmentation neural networks have limitations in recognizing unseen classes. To address this problem, the recent studies draw attentions on zero-shot semantic segmentation. The general zero-shot learning works usually focus on image recognition, which requires to extract transferable features across classes from images. However, zero-shot semantic segmentation needs to transfer knowledge at the pixel level. As semantic classes are defined for the whole objects, it is intuitive that the image-level features are more transferable across classes than the pixel-level features. In this work, we propose the Class2Seg approach for zero-shot semantic segmentation based on the above intuition. The core idea of Class2Seg is to learn transferable image-level features to guide zero-shot semantic segmentation. Our approach contains two branches. One is the image-level classification branch, and the other is the semantic segmentation branch. A cross-task correlation layer is designed to fuse the transferable image-level features into the semantic segmentation branch to promote information transfer from source classes to target classes at the pixel level. Extensive experiments on the Pascal-VOC dataset clearly support the effectiveness of our approach.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanji Hao "Learning transferable image-level features for zero-shot semantic segmentation", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 132570H (10 September 2024); https://doi.org/10.1117/12.3040756

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KEYWORDS

Semantics

Image segmentation

Education and training

Classification systems

Image classification

Image enhancement

Ablation

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