Knowledge Restore and Transfer for Multi-label Class-Incremental Learning

• URL: http://arxiv.org/abs/2302.13334v3
• Date: Mon, 14 Aug 2023 14:35:02 GMT
• Title: Knowledge Restore and Transfer for Multi-label Class-Incremental Learning
• Authors: Songlin Dong, Haoyu Luo, Yuhang He, Xing Wei, Yihong Gong
• Abstract summary: We propose a knowledge restore and transfer (KRT) framework for multi-label class-incremental learning (MLCIL) KRT includes a dynamic pseudo-label (DPL) module to restore the old class knowledge and an incremental cross-attention(ICA) module to save session-specific knowledge and transfer old class knowledge to the new model sufficiently. Experimental results on MS-COCO and PASCAL VOC datasets demonstrate the effectiveness of our method for improving recognition performance and mitigating forgetting.
• Score: 34.378828633726854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current class-incremental learning research mainly focuses on single-label classification tasks while multi-label class-incremental learning (MLCIL) with more practical application scenarios is rarely studied. Although there have been many anti-forgetting methods to solve the problem of catastrophic forgetting in class-incremental learning, these methods have difficulty in solving the MLCIL problem due to label absence and information dilution. In this paper, we propose a knowledge restore and transfer (KRT) framework for MLCIL, which includes a dynamic pseudo-label (DPL) module to restore the old class knowledge and an incremental cross-attention(ICA) module to save session-specific knowledge and transfer old class knowledge to the new model sufficiently. Besides, we propose a token loss to jointly optimize the incremental cross-attention module. Experimental results on MS-COCO and PASCAL VOC datasets demonstrate the effectiveness of our method for improving recognition performance and mitigating forgetting on multi-label class-incremental learning tasks.

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