Branch-Cooperative OSNet for Person Re-Identification

• URL: http://arxiv.org/abs/2006.07206v1
• Date: Fri, 12 Jun 2020 14:09:23 GMT
• Title: Branch-Cooperative OSNet for Person Re-Identification
• Authors: Lei Zhang, Xiaofu Wu, Suofei Zhang and Zirui Yin
• Abstract summary: We propose a branch-cooperative architecture over OSNet, termed BC-OSNet, for person Re-ID. BC-OSNet achieves state-of-art performance on three popular datasets.
• Score: 12.805388189354108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-branch is extensively studied for learning rich feature representation for person re-identification (Re-ID). In this paper, we propose a branch-cooperative architecture over OSNet, termed BC-OSNet, for person Re-ID. By stacking four cooperative branches, namely, a global branch, a local branch, a relational branch and a contrastive branch, we obtain powerful feature representation for person Re-ID. Extensive experiments show that the proposed BC-OSNet achieves state-of-art performance on the three popular datasets, including Market-1501, DukeMTMC-reID and CUHK03. In particular, it achieves mAP of 84.0% and rank-1 accuracy of 87.1% on the CUHK03_labeled.

Related papers

• PoCo: Point Context Cluster for RGBD Indoor Place Recognition [47.12179061883084]
  We present a novel end-to-end algorithm (PoCo) for the indoor RGB-D place recognition task, aimed at identifying the most likely match for a given query frame within a reference database. We propose a new network architecture, which generalizes the recent Context of Clusters (CoCs) to extract global descriptors directly from the noisy point clouds through end-to-end learning.
  arXiv  Detail & Related papers  (2024-04-03T17:38:15Z)
• PersonMAE: Person Re-Identification Pre-Training with Masked AutoEncoders [132.60355401780407]
  Pre-training is playing an increasingly important role in learning generic feature representation for Person Re-identification (ReID) We propose PersonMAE, which involves two core designs into masked autoencoders to better serve the task of Person Re-ID. PersonMAE with ViT-B backbone achieves 79.8% and 69.5% mAP on the MSMT17 and OccDuke datasets.
  arXiv  Detail & Related papers  (2023-11-08T07:02:27Z)
• Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations [54.32381783582341]
  We show that recent advances in self-supervised feature learning enable unsupervised object discovery and semantic segmentation with a performance that matches the state of the field on supervised semantic segmentation 10 years ago. We propose a methodology based on unsupervised saliency masks and self-supervised feature clustering to kickstart object discovery followed by training a semantic segmentation network on pseudo-labels to bootstrap the system on images with multiple objects.
  arXiv  Detail & Related papers  (2022-07-11T17:28:24Z)
• Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline [95.88825497452716]
  Gait benchmarks empower the research community to train and evaluate high-performance gait recognition systems. GREW is the first large-scale dataset for gait recognition in the wild. SPOSGait is the first NAS-based gait recognition model.
  arXiv  Detail & Related papers  (2022-05-05T14:57:39Z)
• Lightweight Multi-Branch Network for Person Re-Identification [6.353193172884524]
  This paper proposes a lightweight network that combines global, part-based, and channel features in a unified multi-branch architecture that builds on the resource-efficient OSNet backbone. Using a well-founded combination of training techniques and design choices, our final model achieves state-of-the-art results on CUHK03 labeled, CUHK03 detected, and Market-1501 with 85.1% mAP / 87.2% rank1, 82.4% mAP / 84.9% rank1, and 91.5% mAP / 96.3% rank1, respectively.
  arXiv  Detail & Related papers  (2021-01-26T13:28:46Z)
• Integrating Coarse Granularity Part-level Features with Supervised Global-level Features for Person Re-identification [3.4758712821739426]
  Part-level person Re-ID network (CGPN) integrates supervised global features for both holistic and partial person images. CGPN learns to extract effective body part features for both holistic and partial person images. Single model trained on three Re-ID datasets including Market-1501, DukeMTMC-reID and CUHK03 state-of-the-art performances.
  arXiv  Detail & Related papers  (2020-10-15T11:49:20Z)
• EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning [82.54669314604097]
  EagleEye is a simple yet efficient evaluation component based on adaptive batch normalization. It unveils a strong correlation between different pruned structures and their final settled accuracy. This module is also general to plug-in and improve some existing pruning algorithms.
  arXiv  Detail & Related papers  (2020-07-06T01:32:31Z)
• A heterogeneous branch and multi-level classification network for person re-identification [13.868524909296553]
  We propose a novel Heterogeneous Branch and Multi-level Classification Network (HBMCN), which is designed based on the pre-trained ResNet-50 model. A new multi-level classification function is proposed for the supervised learning ofCN, whereby multi-level features are extracted from multiple high-level layers and objectived to represent a person.
  arXiv  Detail & Related papers  (2020-06-02T03:34:50Z)
• ResNeSt: Split-Attention Networks [86.25490825631763]
  We present a modularized architecture, which applies the channel-wise attention on different network branches to leverage their success in capturing cross-feature interactions and learning diverse representations. Our model, named ResNeSt, outperforms EfficientNet in accuracy and latency trade-off on image classification.
  arXiv  Detail & Related papers  (2020-04-19T20:40:31Z)
• Learning Diverse Features with Part-Level Resolution for Person Re-Identification [10.940478376944133]
  This paper proposes to construct a strong lightweight network architecture, termed PLR-OSNet. It is based on the idea of Part-Level feature Resolution over the Omni-Scale Network (OSNet) for achieving feature diversity. It achieves state-of-the-art performance on popular person Re-ID datasets, including Market1501, DukeMTMC-reID and CUHK03.
  arXiv  Detail & Related papers  (2020-01-21T11:01:56Z)

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.