REMIND: Retrieval-Augmented Reconstruction With Dual Memories for Modality-Missing Object Re-Identification

To address the modality-missing object Re-Identification (Re-ID) task, a common strategy is to compensate for absent information by exploiting available modalities. However, existing reconstruction-based approaches suffer from two major limitations: 1) they often overlook modality-specific cues inherent in the missing modality; 2) they typically adopt a single-path reconstruction strategy. These issues result in incomplete representations and constrain the capacity to model complex semantic mappings across heterogeneous modalities. To address these challenges, we propose REMIND, a novel framework for modality-missing object Re-Identification, namely REtrieval-AugMented ReconstructIoN With Dual Memories. Specifically, we design a Dual Memory Construction module that, guided by information-theoretic insights, extracts modality-specific and modality-common features through two complementary branches and stores them in dedicated memory banks. These memory banks serve as structured prior knowledge to guide the reconstruction process, ensuring that the features of missing modalities are preserved even under modality-missing conditions. In addition, we have developed a retrieval-augmented missing reconstruction module that enhances the expressiveness and robustness of the reconstruction through multi-path reconstruction and perturbation mechanisms. Adaptive fusion techniques are employed for integration, simultaneously improving the expressiveness and robustness of the reconstructed features. Through the synergy of information-theoretically motivated regularization and retrieval-enhanced reconstruction, REMIND achieves robust feature recovery and delivers highly discriminative representations for reliable modality-missing Re-ID. Extensive experiments on several multi-modal object Re-ID benchmarks demonstrate the effectiveness and superiority of REMIND under various missing modality scenarios. The code is publicly available at: https://github.com/skye-1201/REMIND.