Parallel Data Augmentation for Text-based Person Re-identification

Given textual descriptions, text-based person re-identification aims at retrieving the matched target person in a large-scale image pool. In contrast to the traditional person re-identification (Re-ID) task, text-based person Re-ID requires extra extracted discriminative textual representations and then aligns two modal features to narrow down the semantic gap between linguistic domain and visual domain. A majority of previous works design complex network structures and concatenate multi-branch features while failing to pay much attention to problems with the dataset, which requires more parameters learning and might lead to over-fitting. Hence, in this paper, we propose a Parallel Data Augmentation method (PDA) to reduce over-fitting and make the model occlusion resistant without increasing the number of training parameters. Specifically, prior to the training, for an image, we randomly choose a rectangular region of variable size and erase the region with a constant value. Similar to image processing, we randomly add a mask of random length words to a sentence, then the processed data is sent to the TIPCB framework for training. Extensive experimentations on the large-scale CUHK-PEDES dataset show the effectiveness of our method and verify that our method exceeds the state-of-the-art methods.