Towards Mitigation of False Negatives in Text-to-Image Person Re-Identification

Text-to-image person re-identification (TIReID) aims to retrieve semantically related images from a large gallery given a text query. Most existing TIReID methods train the model on an ideal assumption that positive (negative) samples are semantically correlated (uncorrelated) on the visual and textual modal. However, we observe that incorrect annotation is unavoidable and ambiguous textual description is often taken as the input in practice, which leads to false negatives that ruin the feature aligning between modals for deviated identification. This work presents a novel False Negative Mitigation (FNM) method by identifying potential false negatives through distribution differences and mitigating them with dedicated loss. Specifically, the false negative mitigation loss is designed to adaptively adjust the optimization margin for potential false negatives in the latent space. Moreover, a Dual-Level Feature Representation method is introduced to leverage both global and local features during the mitigation, whereas a Momentum Contractive (MoC) module is plugged to enrich the training data for accurate similarity distribution estimation. We conduct extensive experiments on three public benchmark datasets and the results demonstrate that our FNM method achieves State-of-The-Art (SoTA) performance at all evaluation metrics.