Composition-Incremental Learning for Compositional Generalization

• URL: http://arxiv.org/abs/2511.09082v1
• Date: Thu, 13 Nov 2025 01:30:53 GMT
• Title: Composition-Incremental Learning for Compositional Generalization
• Authors: Zhen Li, Yuwei Wu, Chenchen Jing, Che Sun, Chuanhao Li, Yunde Jia,
• Abstract summary: An ideal model is supposed to gradually improve the capability of compositional generalization in an incremental manner.<n>We develop a benchmark construction pipeline leveraging existing datasets, yielding MIT-States-CompIL and C-GQA-CompIL.<n>We propose a pseudo-replay framework utilizing a visual synthesizer to synthesize visual representations of learned compositions and a linguistic primitive distillation mechanism to maintain aligned primitive representations.
• Score: 35.44592461934844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compositional generalization has achieved substantial progress in computer vision on pre-collected training data. Nonetheless, real-world data continually emerges, with possible compositions being nearly infinite, long-tailed, and not entirely visible. Thus, an ideal model is supposed to gradually improve the capability of compositional generalization in an incremental manner. In this paper, we explore Composition-Incremental Learning for Compositional Generalization (CompIL) in the context of the compositional zero-shot learning (CZSL) task, where models need to continually learn new compositions, intending to improve their compositional generalization capability progressively. To quantitatively evaluate CompIL, we develop a benchmark construction pipeline leveraging existing datasets, yielding MIT-States-CompIL and C-GQA-CompIL. Furthermore, we propose a pseudo-replay framework utilizing a visual synthesizer to synthesize visual representations of learned compositions and a linguistic primitive distillation mechanism to maintain aligned primitive representations across the learning process. Extensive experiments demonstrate the effectiveness of the proposed framework.

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