GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition

• URL: http://arxiv.org/abs/2208.01380v1
• Date: Tue, 2 Aug 2022 11:50:21 GMT
• Title: GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition
• Authors: Beibei Lin, Shunli Zhang, Ming Wang, Lincheng Li, and Xin Yu
• Abstract summary: GaitGL is a global-local based gait recognition network. GLCL is a dual-branch structure that consists of a GFR extractor and a mask-based LFR extractor. Experiments demonstrate GaitGL achieves better performance than state-of-the-art gait recognition methods.
• Score: 29.128348655227416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing gait recognition methods either directly establish Global Feature Representation (GFR) from original gait sequences or generate Local Feature Representation (LFR) from several local parts. However, GFR tends to neglect local details of human postures as the receptive fields become larger in the deeper network layers. Although LFR allows the network to focus on the detailed posture information of each local region, it neglects the relations among different local parts and thus only exploits limited local information of several specific regions. To solve these issues, we propose a global-local based gait recognition network, named GaitGL, to generate more discriminative feature representations. To be specific, a novel Global and Local Convolutional Layer (GLCL) is developed to take full advantage of both global visual information and local region details in each layer. GLCL is a dual-branch structure that consists of a GFR extractor and a mask-based LFR extractor. GFR extractor aims to extract contextual information, e.g., the relationship among various body parts, and the mask-based LFR extractor is presented to exploit the detailed posture changes of local regions. In addition, we introduce a novel mask-based strategy to improve the local feature extraction capability. Specifically, we design pairs of complementary masks to randomly occlude feature maps, and then train our mask-based LFR extractor on various occluded feature maps. In this manner, the LFR extractor will learn to fully exploit local information. Extensive experiments demonstrate that GaitGL achieves better performance than state-of-the-art gait recognition methods. The average rank-1 accuracy on CASIA-B, OU-MVLP, GREW and Gait3D is 93.6%, 98.7%, 68.0% and 63.8%, respectively, significantly outperforming the competing methods. The proposed method has won the first prize in two competitions: HID 2020 and HID 2021.

Related papers

• GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning [84.54244771470012]
  Source-Free Universal Domain Adaptation (SF-UniDA) aims to accurately classify "known" data belonging to common categories. We propose a novel Global and Local Clustering (GLC) technique, which comprises an adaptive one-vs-all global clustering algorithm. We evolve GLC to GLC++, integrating a contrastive affinity learning strategy.
  arXiv  Detail & Related papers  (2024-03-21T13:57:45Z)
• Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation [59.24824050194334]
  This paper turns to an interesting working mechanism in agent learning named Complementary Learning System ( CLS) Motivated by this simple but effective learning pattern, we propose a General-Specific Learning Mechanism (GSLM) GSLM develops a General Learning Module (GLM) and a Specific Learning Module (SLM)
  arXiv  Detail & Related papers  (2023-03-04T16:16:47Z)
• SuperGF: Unifying Local and Global Features for Visual Localization [13.869227429939423]
  SuperGF is a transformer-based aggregation model that operates directly on image-matching-specific local features. We provide implementations of SuperGF using various types of local features, including dense and sparse learning-based or hand-crafted descriptors.
  arXiv  Detail & Related papers  (2022-12-23T13:48:07Z)
• L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation [67.26984058377435]
  We present L2G, a simple online local-to-global knowledge transfer framework for high-quality object attention mining. Our framework conducts the global network to learn the captured rich object detail knowledge from a global view. Experiments show that our method attains 72.1% and 44.2% mIoU scores on the validation set of PASCAL VOC 2012 and MS COCO 2014.
  arXiv  Detail & Related papers  (2022-04-07T04:31:32Z)
• Region-Based Semantic Factorization in GANs [67.90498535507106]
  We present a highly efficient algorithm to factorize the latent semantics learned by Generative Adversarial Networks (GANs) concerning an arbitrary image region. Through an appropriately defined generalized Rayleigh quotient, we solve such a problem without any annotations or training. Experimental results on various state-of-the-art GAN models demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2022-02-19T17:46:02Z)
• Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning [6.144554939661599]
  We propose hand-based person identification by learning both global and local deep feature representation. Our proposed method, Global and Part-Aware Network (GPA-Net), creates global and local branches on the conv-layer. We make extensive evaluations on two large multi-ethnic and publicly available hand datasets, demonstrating that our proposed method significantly outperforms competing approaches.
  arXiv  Detail & Related papers  (2021-01-13T18:47:47Z)
• PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation [87.50205728818601]
  We propose a PriorGuided Local (PGL) self-supervised model that learns the region-wise local consistency in the latent feature space. Our PGL model learns the distinctive representations of local regions, and hence is able to retain structural information.
  arXiv  Detail & Related papers  (2020-11-25T11:03:11Z)
• Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation [28.721376937882958]
  Gait recognition is one of the most important biometric technologies and has been applied in many fields. Recent gait recognition frameworks represent each gait frame by descriptors extracted from either global appearances or local regions of humans. We propose a novel feature extraction and fusion framework to achieve discriminative feature representations for gait recognition.
  arXiv  Detail & Related papers  (2020-11-03T04:07:13Z)
• Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition [75.4027660840568]
  This paper explores how to enhance the local and global dense feature flow by exploiting hierarchical features fully from all the convolution layers. Technically, we propose an efficient and effective CNN framework, i.e., Fast Dense Residual Network (FDRN) for text recognition.
  arXiv  Detail & Related papers  (2020-01-23T06:55:08Z)

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.