Weak-shot Semantic Segmentation by Transferring Semantic Affinity and Boundary

• URL: http://arxiv.org/abs/2110.01519v1
• Date: Mon, 4 Oct 2021 15:37:25 GMT
• Title: Weak-shot Semantic Segmentation by Transferring Semantic Affinity and Boundary
• Authors: Siyuan Zhou and Li Niu and Jianlou Si and Chen Qian and Liqing Zhang
• Abstract summary: We show that existing fully-annotated base categories can help segment objects of novel categories with only image-level labels. We propose a method under the WSSS framework to transfer semantic affinity and boundary from base categories to novel ones.
• Score: 23.331708585468814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly-supervised semantic segmentation (WSSS) with image-level labels has been widely studied to relieve the annotation burden of the traditional segmentation task. In this paper, we show that existing fully-annotated base categories can help segment objects of novel categories with only image-level labels, even if base and novel categories have no overlap. We refer to this task as weak-shot semantic segmentation, which could also be treated as WSSS with auxiliary fully-annotated categories. Recent advanced WSSS methods usually obtain class activation maps (CAMs) and refine them by affinity propagation. Based on the observation that semantic affinity and boundary are class-agnostic, we propose a method under the WSSS framework to transfer semantic affinity and boundary from base categories to novel ones. As a result, we find that pixel-level annotation of base categories can facilitate affinity learning and propagation, leading to higher-quality CAMs of novel categories. Extensive experiments on PASCAL VOC 2012 dataset demonstrate that our method significantly outperforms WSSS baselines on novel categories.

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