Dual Uncertainty-Aware Correspondence Adapting and Retaining for Continual Composed Image Retrieval

Recent research in continual learning has primarily focused on unimodal tasks, with limited attention to multimodal tasks such as Composed Image Retrieval (CIR). In this paper, we establish a novel Continual CIR setting named C2IR to simulate the ever-change retrieval demands in the real world. Using the C2IR setting, we identify two significant challenges: intra-task correspondence uncertainty, which hinders the model's ability to manage noisy query-target pair correspondences; and inter-task drift uncertainty, which impedes the model's consistent understanding of relationships, exacerbating catastrophic forgetting across continual tasks. To address these challenges, we propose a Dual Uncertainty-aware Correspondence Adapting and Retaining (U2CAR) framework for C2IR, which leverages uncertainty learning to acquire and consolidate composed correspondence. To ensure reliable composed correspondence inference in each task, we introduce an Uncertainty-based Correspondence Reasoning (UCR) module that estimates and refines the uncertainty in query-target correspondence. Besides, to mitigate catastrophic forgetting of previous tasks, we design an Uncertainty-guided Re-parameterization (URep) paradigm that consolidates valuable composed correspondence knowledge based on the uncertainty variance across various tasks. Extensive experimental results illustrate that our U2CAR significantly outperforms existing methods, demonstrating the robust adaptability and anti-forgetting capabilities of the proposed approach.