Not Just Object, But State: Compositional Incremental Learning without Forgetting

Yanyi Zhang; Binglin Qiu; Qi Jia; Yu Liu; Ran He

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arXiv · 2411.01739

Not Just Object, But State: Compositional Incremental Learning without Forgetting

Yanyi Zhang·Binglin Qiu·Qi Jia·Yu Liu·Ran He

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Abstract

Most incremental learners excessively prioritize coarse classes of objects while neglecting various kinds of states (e.g. color and material) attached to the objects. As a result, they are limited in the ability to reason fine-grained compositionality of state-object pairs. To remedy this limitation, we propose a novel task called Compositional Incremental Learning (composition-IL), enabling the model to recognize state-object compositions as a whole in an incremental learning fashion. Since the lack of suitable benchmarks, we re-organize two existing datasets and make them tailored for composition-IL. Then, we propose a prompt-based Composition Incremental Learner (CompILer), to overcome the ambiguous composition boundary problem which challenges composition-IL largely. Specifically, we exploit multi-pool prompt learning, which is regularized by inter-pool prompt discrepancy and intra-pool prompt diversity. Besides, we devise object-injected state prompting by using object prompts to guide the selection of state prompts. Furthermore, we fuse the selected prompts by a generalized-mean strategy, to eliminate irrelevant information learned in the prompts. Extensive experiments on two datasets exhibit state-of-the-art performance achieved by CompILer.

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BibTeX RIS

Yanyi Zhang, Binglin Qiu, Qi Jia, Yu Liu, Ran He. 2025-03-27. Not Just Object, But State: Compositional Incremental Learning without Forgetting. https://arxiv.org/abs/2411.01739

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