Miss-ReID: Delivering Robust Multi-Modality Object Re-Identification Despite Missing Modalities

Multi-modality object Re-IDentification (ReID) targets to retrieve special objects by integrating complementary information from diverse visual sources. However, existing models that are trained on modality-complete datasets typically exhibit significantly degraded discrimination during inference with modality-incomplete inputs. This disparity highlights the necessity of developing a robust multi-modality ReID model that remains effective in real-world applications. For that, this paper delivers a flexible framework tailored for more realistic multi-modality retrieval scenario, dubbed as Miss-ReID, which is the first work to friendly support both the modality-missing training and inference conditions. The core of Miss-ReID lies in compensating for missing visual cues via vision-text knowledge transfer driven by Vision-Language foundation Models (VLMs), effectively mitigating performance degradation.