Diverse Embedding Modeling with Adaptive Noise Filter for Text-based Person Retrieval
Chen He, Shenshen Li, Zheng Wang, Fumin Shen, Yang Yang, Xing Xu · 2024
Text-based person retrieval (TBPR) involves retrieving pedestrian images from a gallery using textual queries. Existing methods assume that all the training pairs are correct and the textual query only corresponds to one image. However, in practical scenarios of TBPR, there indeed exists data noise and many-to-many matching relationships between semantically similar images and textual queries. To address these problems, we propose a novel approach termed Diverse Embedding Modeling (DEM) with Adaptive Noise Filter for the TBPR task. Firstly, we propose a dynamic margin to measure the degree of noise, which can adaptively reduce the weights of image-text pairs with severe noise during training, thereby effectively mitigating the impact of noisy pairs. Moreover, we model diverse visual and textual embeddings from learnable parameterized distributions, which aim to simulate the many-to-many matching scenarios. Extensive experiments conducted on three TBPR datasets demonstrate the superior performance of our DEM method compared to recent state-of-the-art methods.