# Attribute-aware Identity-hard Triplet Loss for Video-based Person Re-identification

arXiv: https://arxiv.org/abs/2006.07597 · HTML: https://mlsanity.com/paper/2006.07597/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Causality and "In-the-Wild" Video-Based Person Re-ID: A Survey](https://mlsanity.com/paper/2505.20540/) (2025)

## Abstract (arXiv)

Video-based person re-identification (Re-ID) is an important computer vision task. The batch-hard triplet loss frequently used in video-based person Re-ID suffers from the Distance Variance among Different Positives (DVDP) problem. In this paper, we address this issue by introducing a new metric learning method called Attribute-aware Identity-hard Triplet Loss (AITL), which reduces the intra-class variation among positive samples via calculating attribute distance. To achieve a complete model of video-based person Re-ID, a multi-task framework with Attribute-driven Spatio-Temporal Attention (ASTA) mechanism is also proposed. Extensive experiments on MARS and DukeMTMC-VID datasets shows that both the AITL and ASTA are very effective. Enhanced by them, even a simple light-weighted video-based person Re-ID baseline can outperform existing state-of-the-art approaches. The codes has been published on https://github.com/yuange250/Video-based-person-ReID-with-Attribute-information.