Zero-Shot Semantic Segmentation via Spatial and Multi-Scale Aware Visual Class Embedding

• URL: http://arxiv.org/abs/2111.15181v1
• Date: Tue, 30 Nov 2021 07:39:19 GMT
• Title: Zero-Shot Semantic Segmentation via Spatial and Multi-Scale Aware Visual Class Embedding
• Authors: Sungguk Cha and Yooseung Wang
• Abstract summary: We propose a language-model-free zero-shot semantic segmentation framework, Spatial and Multi-scale aware Visual Class Embedding Network (SM-VCENet) In experiments, our SM-VCENet outperforms zero-shot semantic segmentation state-of-the-art by a relative margin.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fully supervised semantic segmentation technologies bring a paradigm shift in scene understanding. However, the burden of expensive labeling cost remains as a challenge. To solve the cost problem, recent studies proposed language model based zero-shot semantic segmentation (L-ZSSS) approaches. In this paper, we address L-ZSSS has a limitation in generalization which is a virtue of zero-shot learning. Tackling the limitation, we propose a language-model-free zero-shot semantic segmentation framework, Spatial and Multi-scale aware Visual Class Embedding Network (SM-VCENet). Furthermore, leveraging vision-oriented class embedding SM-VCENet enriches visual information of the class embedding by multi-scale attention and spatial attention. We also propose a novel benchmark (PASCAL2COCO) for zero-shot semantic segmentation, which provides generalization evaluation by domain adaptation and contains visually challenging samples. In experiments, our SM-VCENet outperforms zero-shot semantic segmentation state-of-the-art by a relative margin in PASCAL-5i benchmark and shows generalization-robustness in PASCAL2COCO benchmark.

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