# Progressive Learning Algorithm for Efficient Person Re-Identification

arXiv: https://arxiv.org/abs/1912.07447 · HTML: https://mlsanity.com/paper/1912.07447/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2019.

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

- [Unsupervised Visible-Infrared ReID via Pseudo-label Correction and Modality-level Alignment](https://mlsanity.com/paper/2404.06683/) (2024)

## Abstract (arXiv)

This paper studies the problem of Person Re-Identification (ReID)for large-scale applications. Recent research efforts have been devoted to building complicated part models, which introduce considerably high computational cost and memory consumption, inhibiting its practicability in large-scale applications. This paper aims to develop a novel learning strategy to find efficient feature embeddings while maintaining the balance of accuracy and model complexity. More specifically, we find by enhancing the classical triplet loss together with cross-entropy loss, our method can explore the hard examples and build a discriminant feature embedding yet compact enough for large-scale applications. Our method is carried out progressively using Bayesian optimization, and we call it the Progressive Learning Algorithm (PLA). Extensive experiments on three large-scale datasets show that our PLA is comparable or better than the-state-of-the-arts. Especially, on the challenging Market-1501 dataset, we achieve Rank-1=94.7%/mAP=89.4% while saving at least 30% parameters than strong part models.