Strong but Simple Baseline with Dual-Granularity Triplet Loss for Visible-Thermal Person Re-Identification

• URL: http://arxiv.org/abs/2012.05010v2
• Date: Wed, 10 Mar 2021 08:01:33 GMT
• Title: Strong but Simple Baseline with Dual-Granularity Triplet Loss for Visible-Thermal Person Re-Identification
• Authors: Haijun Liu, Yanxia Chai, Xiaoheng Tan, Dong Li and Xichuan Zhou
• Abstract summary: We propose a conceptually simple and effective dual-granularity triplet loss for visible-thermal person re-identification (VT-ReID) Our proposed dual-granularity triplet loss well organizes the sample-based triplet loss and center-based triplet loss in a hierarchical fine to coarse granularity manner.
• Score: 9.964287254346976
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this letter, we propose a conceptually simple and effective dual-granularity triplet loss for visible-thermal person re-identification (VT-ReID). In general, ReID models are always trained with the sample-based triplet loss and identification loss from the fine granularity level. It is possible when a center-based loss is introduced to encourage the intra-class compactness and inter-class discrimination from the coarse granularity level. Our proposed dual-granularity triplet loss well organizes the sample-based triplet loss and center-based triplet loss in a hierarchical fine to coarse granularity manner, just with some simple configurations of typical operations, such as pooling and batch normalization. Experiments on RegDB and SYSU-MM01 datasets show that with only the global features our dual-granularity triplet loss can improve the VT-ReID performance by a significant margin. It can be a strong VT-ReID baseline to boost future research with high quality.

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