Beyond triplet loss: Person re-identification with fine-grained difference-aware pairwise loss

• Main Authors: YAN, Cheng, PANG, Guansong, BAI, Xiao, LIU, Changhong, NING, Xin, ZHOU, Jun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Person Re-Identification; Fine-grained Difference; Representation Learning; Triplet Loss; Pairwise Loss; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7023; https://ink.library.smu.edu.sg/context/sis_research/article/8026/viewcontent/2009.10295.pdf
• Institution: Singapore Management University

Person Re-IDentification (ReID) aims at re-identifying persons from different viewpoints across multiple cameras. Capturing the fine-grained appearance differences is often the key to accurate person ReID, because many identities can be differentiated only when looking into these fine-grained differences. However, most state-of-the-art person ReID approaches, typically driven by a triplet loss, fail to effectively learn the fine-grained features as they are focused more on differentiating large appearance differences. To address this issue, we introduce a novel pairwise loss function that enables ReID models to learn the fine-grained features by adaptively enforcing an exponential penalization on the images of small differences and a bounded penalization on the images of large differences. The proposed loss is generic and can be used as a plugin to replace the triplet loss to significantly enhance different types of state-of-the-art approaches. Experimental results on four benchmark datasets show that the proposed loss substantially outperforms a number of popular loss functions by large margins; and it also enables significantly improved data efficiency.