Computer Science > Computer Vision and Pattern Recognition
[Submitted on 19 Feb 2020 (v1), last revised 22 Oct 2020 (this version, v2)]
Title:Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond
View PDFAbstract:Person re-identification (re-ID) aims at matching images of the same person across camera views. Due to varying distances between cameras and persons of interest, resolution mismatch can be expected, which would degrade 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 re-ID performance due to preserving resolution invariance and recovering re-ID oriented discriminative details. Extensive experimental results on five standard person re-ID benchmarks confirm the effectiveness of our method and the superiority over the state-of-the-art approaches, especially when the input resolutions are not seen during training. Furthermore, the experimental results on two vehicle re-ID benchmarks also confirm the generalization of our model on cross-resolution visual tasks. The extensions of semi-supervised settings further support the use of our proposed approach to real-world scenarios and applications.
Submission history
From: Yu-Jhe Li [view email][v1] Wed, 19 Feb 2020 07:21:38 UTC (14,609 KB)
[v2] Thu, 22 Oct 2020 18:01:01 UTC (27,303 KB)
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