Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification
arXiv:1908.06052
Abstract
Person re-identification (re-ID) aims at matching images of the same identity across camera views. Due to varying distances between cameras and persons of interest, resolution mismatch can be expected, which would degrade person re-ID performance in real-world scenarios. To overcome this problem, we propose a novel generative adversarial network to address cross-resolution person re-ID, allowing query images with varying resolutions. By advancing adversarial learning techniques, our proposed model learns resolution-invariant image representations while being able to recover the missing details in low-resolution input images. The resulting features can be jointly applied for improving person re-ID performance due to preserving resolution invariance and recovering re-ID oriented discriminative details. Our experiments on five benchmark datasets confirm the effectiveness of our approach and its superiority over the state-of-the-art methods, especially when the input resolutions are unseen during training.
arXiv commentAccepted to ICCV 2019
References in corpus (10)
- In Defense of the Triplet Loss for Person Re-Identification
- Person Re-identification: Past, Present and Future
- Improving Person Re-identification by Attribute and Identity Learning
- The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
- FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
- Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
- Harmonious Attention Network for Person Re-Identification
- Deep Group-shuffling Random Walk for Person Re-identification
- Person Re-identification for Real-world Surveillance Systems
- Person Re-identification with Deep Similarity-Guided Graph Neural Network