Unsupervised Vehicle Re-Identification via Self-supervised Metric Learning using Feature Dictionary

• URL: http://arxiv.org/abs/2103.02250v1
• Date: Wed, 3 Mar 2021 08:29:03 GMT
• Title: Unsupervised Vehicle Re-Identification via Self-supervised Metric Learning using Feature Dictionary
• Authors: Jongmin Yu, Hyeontaek Oh
• Abstract summary: Key challenge of unsupervised vehicle re-identification (Re-ID) is learning discriminative features from unlabelled vehicle images. This paper addresses an unsupervised vehicle Re-ID method, which no need any types of a labelled dataset.
• Score: 1.7894377200944507
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The key challenge of unsupervised vehicle re-identification (Re-ID) is learning discriminative features from unlabelled vehicle images. Numerous methods using domain adaptation have achieved outstanding performance, but those methods still need a labelled dataset as a source domain. This paper addresses an unsupervised vehicle Re-ID method, which no need any types of a labelled dataset, through a Self-supervised Metric Learning (SSML) based on a feature dictionary. Our method initially extracts features from vehicle images and stores them in a dictionary. Thereafter, based on the dictionary, the proposed method conducts dictionary-based positive label mining (DPLM) to search for positive labels. Pair-wise similarity, relative-rank consistency, and adjacent feature distribution similarity are jointly considered to find images that may belong to the same vehicle of a given probe image. The results of DPLM are applied to dictionary-based triplet loss (DTL) to improve the discriminativeness of learnt features and to refine the quality of the results of DPLM progressively. The iterative process with DPLM and DTL boosts the performance of unsupervised vehicle Re-ID. Experimental results demonstrate the effectiveness of the proposed method by producing promising vehicle Re-ID performance without a pre-labelled dataset. The source code for this paper is publicly available on `https://github.com/andreYoo/VeRI_SSML_FD.git'.

Related papers

• DSAM-GN:Graph Network based on Dynamic Similarity Adjacency Matrices for Vehicle Re-identification [16.692943669382064]
  This paper proposes a method, named graph network based on dynamic similarity adjacency matrices (DSAM-GN) The proposed method divides the extracted vehicle features into different patches as nodes within the graph network. Experimental results on public datasets VeRi-776 and VehicleID demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2023-10-25T15:04:57Z)
• Disambiguation of Company names via Deep Recurrent Networks [101.90357454833845]
  We propose a Siamese LSTM Network approach to extract -- via supervised learning -- an embedding of company name strings. We analyse how an Active Learning approach to prioritise the samples to be labelled leads to a more efficient overall learning pipeline.
  arXiv  Detail & Related papers  (2023-03-07T15:07:57Z)
• Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification [80.98291772215154]
  Unsupervised person re-identification (ReID) aims at learning discriminative identity features for person retrieval without any annotations. Recent advances accomplish this task by leveraging clustering-based pseudo labels. We propose a Neighbour Consistency guided Pseudo Label Refinement framework.
  arXiv  Detail & Related papers  (2022-11-30T09:39:57Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• Discriminative Dictionary Learning based on Statistical Methods [0.0]
  Sparse Representation (SR) of signals or data has a well founded theory with rigorous mathematical error bounds and proofs. Training dictionaries such that they represent each class of signals with minimal loss is called Dictionary Learning (DL) MOD and K-SVD have been successfully used in reconstruction based applications in image processing like image "denoising", "inpainting"
  arXiv  Detail & Related papers  (2021-11-17T10:45:10Z)
• Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-Identification [4.5471611558189124]
  We propose camera-tracklet-aware contrastive learning (CTACL) using the multi-camera tracklet information without vehicle identity labels. The proposed CTACL divides an unlabelled domain, i.e., entire vehicle images, into multiple camera-level images and conducts contrastive learning. We demonstrate the effectiveness of our approach on video-based and image-based vehicle Re-ID datasets.
  arXiv  Detail & Related papers  (2021-09-14T02:12:54Z)
• Group-aware Label Transfer for Domain Adaptive Person Re-identification [179.816105255584]
  Unsupervised Adaptive Domain (UDA) person re-identification (ReID) aims at adapting the model trained on a labeled source-domain dataset to a target-domain dataset without any further annotations. Most successful UDA-ReID approaches combine clustering-based pseudo-label prediction with representation learning and perform the two steps in an alternating fashion. We propose a Group-aware Label Transfer (GLT) algorithm, which enables the online interaction and mutual promotion of pseudo-label prediction and representation learning.
  arXiv  Detail & Related papers  (2021-03-23T07:57:39Z)
• Unsupervised Pretraining for Object Detection by Patch Reidentification [72.75287435882798]
  Unsupervised representation learning achieves promising performances in pre-training representations for object detectors. This work proposes a simple yet effective representation learning method for object detection, named patch re-identification (Re-ID) Our method significantly outperforms its counterparts on COCO in all settings, such as different training iterations and data percentages.
  arXiv  Detail & Related papers  (2021-03-08T15:13:59Z)
• Unsupervised Domain Adaptation for Person Re-Identification through Source-Guided Pseudo-Labeling [2.449909275410288]
  Person Re-Identification (re-ID) aims at retrieving images of the same person taken by different cameras. Unsupervised Domain Adaptation (UDA) is an interesting research direction for this challenge as it avoids a costly annotation of the target data. We introduce a framework which relies on a two-branch architecture optimizing classification and triplet loss based metric learning in source and target domains.
  arXiv  Detail & Related papers  (2020-09-20T14:54:42Z)
• CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions [61.724894233252414]
  This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem. Existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering. We introduce a different unsupervised method that allows us to learn pedestrian embeddings from raw videos, without resorting to pseudo labels.
  arXiv  Detail & Related papers  (2020-07-15T09:52:35Z)
• Unsupervised Vehicle Re-identification with Progressive Adaptation [26.95027290004128]
  Vehicle re-identification (reID) aims at identifying vehicles across different non-overlapping cameras views. We propose a novel progressive adaptation learning method for vehicle reID, named PAL, which infers from the abundant data without annotations.
  arXiv  Detail & Related papers  (2020-06-20T03:59:41Z)

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.