Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification

• URL: http://arxiv.org/abs/2207.13037v1
• Date: Sat, 9 Jul 2022 03:49:51 GMT
• Title: Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification
• Authors: Lin Wu, Lingqiao Liu, Yang Wang, Zheng Zhang, Farid Boussaid, Mohammed Bennamoun
• Abstract summary: Cross-resolution person re-identification problem aims to match low-resolution (LR) query identity images against high resolution (HR) gallery images. It is a challenging and practical problem since the query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras. This paper explores an alternative SR-free paradigm to directly compare HR and LR images via a dynamic metric, which is adaptive to the resolution of a query image.
• Score: 49.57112924976762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The cross-resolution person re-identification (CRReID) problem aims to match low-resolution (LR) query identity images against high resolution (HR) gallery images. It is a challenging and practical problem since the query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras. To address this problem, state-of-the-art (SOTA) solutions either learn the resolution-invariant representation or adopt super-resolution (SR) module to recover the missing information from the LR query. This paper explores an alternative SR-free paradigm to directly compare HR and LR images via a dynamic metric, which is adaptive to the resolution of a query image. We realize this idea by learning resolution-adaptive representations for cross-resolution comparison. Specifically, we propose two resolution-adaptive mechanisms. The first one disentangles the resolution-specific information into different sub-vectors in the penultimate layer of the deep neural networks, and thus creates a varying-length representation. To better extract resolution-dependent information, we further propose to learn resolution-adaptive masks for intermediate residual feature blocks. A novel progressive learning strategy is proposed to train those masks properly. These two mechanisms are combined to boost the performance of CRReID. Experimental results show that the proposed method is superior to existing approaches and achieves SOTA performance on multiple CRReID benchmarks.

Related papers

• Continuous Cross-resolution Remote Sensing Image Change Detection [28.466756872079472]
  Real-world applications raise the need for cross-resolution change detection, aka, CD based on bitemporal images with different spatial resolutions. We propose scale-invariant learning to enforce the model consistently predicting HR results given synthesized samples of varying resolution differences. Our method significantly outperforms several vanilla CD methods and two cross-resolution CD methods on three datasets.
  arXiv  Detail & Related papers  (2023-05-24T04:57:24Z)
• Reference-based Image and Video Super-Resolution via C2-Matching [100.0808130445653]
  We propose C2-Matching, which performs explicit robust matching crossing transformation and resolution. C2-Matching significantly outperforms state of the arts on the standard CUFED5 benchmark. We also extend C2-Matching to Reference-based Video Super-Resolution task, where an image taken in a similar scene serves as the HR reference image.
  arXiv  Detail & Related papers  (2022-12-19T16:15:02Z)
• Resolution based Feature Distillation for Cross Resolution Person Re-Identification [17.86505685442293]
  Person re-identification (re-id) aims to retrieve images of same identities across different camera views. Resolution mismatch occurs due to varying distances between person of interest and cameras. We propose a Resolution based Feature Distillation (RFD) approach to overcome the problem of multiple resolutions.
  arXiv  Detail & Related papers  (2021-09-16T11:07:59Z)
• MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution [74.24676600271253]
  We propose the MASA network for RefSR, where two novel modules are designed to address these problems. The proposed Match & Extraction Module significantly reduces the computational cost by a coarse-to-fine correspondence matching scheme. The Spatial Adaptation Module learns the difference of distribution between the LR and Ref images, and remaps the distribution of Ref features to that of LR features in a spatially adaptive way.
  arXiv  Detail & Related papers  (2021-06-04T07:15:32Z)
• Robust Reference-based Super-Resolution via C2-Matching [77.51610726936657]
  Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image by introducing an additional high-resolution (HR) reference image. Existing Ref-SR methods mostly rely on implicit correspondence matching to borrow HR textures from reference images to compensate for the information loss in input images. We propose C2-Matching, which produces explicit robust matching crossing transformation and resolution.
  arXiv  Detail & Related papers  (2021-06-03T16:40:36Z)
• Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification [37.01666917620271]
  Cross-resolution person re-ID aims to match person images captured from non-overlapped cameras. emphtextbfMulti-Resolution textbfRepresentations textbfJoint textbfLearning (textbfMRJL) Our method consists of a Resolution Reconstruction Network (RRN) and a Dual Feature Fusion Network (DFFN)
  arXiv  Detail & Related papers  (2021-05-26T16:54:56Z)
• Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identification [22.104449922937338]
  Person re-identification (re-ID) tackles the problem of matching person images with the same identity from different cameras. We propose a Deep High-Resolution Pseudo-Siamese Framework (PS-HRNet) to solve the problem. Our proposed PS-HRNet improves 3.4%, 6.2%, 2.5%,1.1% and 4.2% at Rank-1 on MLR-Market-1501, MLR-CUHK03, MLR-VIPeR, MLR-DukeMTMC-reID, and CAVIAR datasets.
  arXiv  Detail & Related papers  (2021-05-25T07:45:38Z)
• Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition [59.29808528182607]
  Cross-resolution face recognition (CRFR) is important in intelligent surveillance and biometric forensics. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space. In this study, we desire to fully exploit the multi-level deep convolutional neural network (CNN) feature set for robust CRFR.
  arXiv  Detail & Related papers  (2021-03-25T14:03:42Z)
• Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond [59.149653740463435]
  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. We propose a novel generative adversarial network to address cross-resolution person re-ID.
  arXiv  Detail & Related papers  (2020-02-19T07:21:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.