Composed Image Retrieval (CIR) combines the reference image with text to retrieve the intended target image. Recently, zero-shot CIR has gained significant attention by eliminating the need for labeled triplets required in supervised CIR. However, it inevitably demands additional training corpus, storage, and computational resources, limiting its applicability in real-world scenarios. Inspired by advancements in Test-Time Adaptation (TTA), we propose a Test-Time CIR setting named TT-CIR, which aims to efficiently adapt models to unlabeled test samples while reducing resource consumption. Within the TT-CIR setting, we identify that naively introducing existing TTA methods (e.g., reward-based) into CIR faces two vital challenges: 1) Modification-restricted reward pool, which limits the exploration of semantically relevant candidate rewards; 2) Conservative knowledge feedback, which inhibits the adaptability of rewards to the current data distribution. To address these challenges, we propose a test-time reinforcement learning framework that integrates a Counterfactual-guided Multinomial Sampling (CMS) strategy and a Duplex Rewards Modeling (DRM) module. The CMS explores a candidate reward pool that is visually similar and semantically relevant to the given query, while the DRM generates stable and adaptive duplex rewards to guide model adaptation. Extensive experiments demonstrate the superiority and adaptability of our method over existing approaches.

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.

Facial expression recognition (FER) remains a challenging task due to the ambiguity and subtlety of expressions. To address this challenge, current FER methods predominantly prioritize visual cues while inadvertently neglecting the potential insights that can be gleaned from other modalities. Recently, vision-language pre-training (VLP) models integrated textual cues as guidance, culminating in a powerful multi-modal solution that has proven effective for a range of computer vision tasks. In this paper, we propose a Cross-Modal Emotion-Aware Prompting (CEPrompt) framework for FER based on VLP models. To make VLP models sensitive to expression-relevant visual discrepancies, we devise an Emotion Conception-guided Visual Adapter (EVA) to capture the category-specific appearance representations with emotion conception guidance. Moreover, knowledge distillation is employed to prevent the model from forgetting the pre-trained category-invariant knowledge. In addition, we design a Conception-Appearance Tuner (CAT) to facilitate the interaction of multi-modal information via cooperatively tuning between emotion conception and appearance prompts. In this way, semantic information about emotion text conception is infused directly into facial appearance images, thereby enhancing a comprehensive and precise understanding of expression-related facial details. Quantitative and qualitative experiments show that our CEPrompt outperforms state-of-the-art approaches on three real-world FER datasets. The code is available athttps://github.com/HaoliangZhou/CEPrompt.