# Sharp Attention Network via Adaptive Sampling for Person Re-identification

arXiv: https://arxiv.org/abs/1805.02336 · HTML: https://mlsanity.com/paper/1805.02336/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2018.

## Built on by (most cited first, 3 of 3)

- [Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters](https://mlsanity.com/paper/2311.18763/) (2023)
- [MeSIN: Multilevel Selective and Interactive Network for Medication Recommendation](https://mlsanity.com/paper/2104.11026/) (2021)
- [Domain-adaptive Person Re-identification without Cross-camera Paired Samples](https://mlsanity.com/paper/2307.06533/) (2023)

## Abstract (arXiv)

In this paper, we present novel sharp attention networks by adaptively sampling feature maps from convolutional neural networks (CNNs) for person re-identification (re-ID) problem. Due to the introduction of sampling-based attention models, the proposed approach can adaptively generate sharper attention-aware feature masks. This greatly differs from the gating-based attention mechanism that relies soft gating functions to select the relevant features for person re-ID. In contrast, the proposed sampling-based attention mechanism allows us to effectively trim irrelevant features by enforcing the resultant feature masks to focus on the most discriminative features. It can produce sharper attentions that are more assertive in localizing subtle features relevant to re-identifying people across cameras. For this purpose, a differentiable Gumbel-Softmax sampler is employed to approximate the Bernoulli sampling to train the sharp attention networks. Extensive experimental evaluations demonstrate the superiority of this new sharp attention model for person re-ID over the other state-of-the-art methods on three challenging benchmarks including CUHK03, Market-1501, and DukeMTMC-reID.