Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation

• URL: http://arxiv.org/abs/2305.10868v1
• Date: Thu, 18 May 2023 10:40:52 GMT
• Title: Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation
• Authors: Yuan Zhou, Xin Chen, Yanrong Guo, Shijie Hao, Richang Hong, Qi Tian
• Abstract summary: Incremental few-shot semantic segmentation aims to incrementally extend a semantic segmentation model to novel classes. This task faces a severe semantic-aliasing issue between base and novel classes due to data imbalance. We propose the Semantic-guided Relation Alignment and Adaptation (SRAA) method that fully considers the guidance of prior semantic information.
• Score: 98.51938442785179
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Incremental few-shot semantic segmentation (IFSS) aims to incrementally extend a semantic segmentation model to novel classes according to only a few pixel-level annotated data, while preserving its segmentation capability on previously learned base categories. This task faces a severe semantic-aliasing issue between base and novel classes due to data imbalance, which makes segmentation results unsatisfactory. To alleviate this issue, we propose the Semantic-guided Relation Alignment and Adaptation (SRAA) method that fully considers the guidance of prior semantic information. Specifically, we first conduct Semantic Relation Alignment (SRA) in the base step, so as to semantically align base class representations to their semantics. As a result, the embeddings of base classes are constrained to have relatively low semantic correlations to categories that are different from them. Afterwards, based on the semantically aligned base categories, Semantic-Guided Adaptation (SGA) is employed during the incremental learning stage. It aims to ensure affinities between visual and semantic embeddings of encountered novel categories, thereby making the feature representations be consistent with their semantic information. In this way, the semantic-aliasing issue can be suppressed. We evaluate our model on the PASCAL VOC 2012 and the COCO dataset. The experimental results on both these two datasets exhibit its competitive performance, which demonstrates the superiority of our method.

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