# DARI: Distance metric And Representation Integration for Person Verification

arXiv: https://arxiv.org/abs/1604.04377 · HTML: https://mlsanity.com/paper/1604.04377/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2016.

## Built on by (most cited first, 5 of 5)

- [Solving Inefficiency of Self-supervised Representation Learning](https://mlsanity.com/paper/2104.08760/) (2021)
- [PartMix: Regularization Strategy to Learn Part Discovery for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2304.01537/) (2023)
- [Learning Instance-level Spatial-Temporal Patterns for Person Re-identification](https://mlsanity.com/paper/2108.00171/) (2021)
- [MSCMNet: Multi-scale Semantic Correlation Mining for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2311.14395/) (2023)
- [Contrastive Multiple Instance Learning for Weakly Supervised Person ReID](https://mlsanity.com/paper/2402.07685/) (2024)

## Abstract (arXiv)

The past decade has witnessed the rapid development of feature representation learning and distance metric learning, whereas the two steps are often discussed separately. To explore their interaction, this work proposes an end-to-end learning framework called DARI, i.e. Distance metric And Representation Integration, and validates the effectiveness of DARI in the challenging task of person verification. Given the training images annotated with the labels, we first produce a large number of triplet units, and each one contains three images, i.e. one person and the matched/mismatch references. For each triplet unit, the distance disparity between the matched pair and the mismatched pair tends to be maximized. We solve this objective by building a deep architecture of convolutional neural networks. In particular, the Mahalanobis distance matrix is naturally factorized as one top fully-connected layer that is seamlessly integrated with other bottom layers representing the image feature. The image feature and the distance metric can be thus simultaneously optimized via the one-shot backward propagation. On several public datasets, DARI shows very promising performance on re-identifying individuals cross cameras against various challenges, and outperforms other state-of-the-art approaches.