vehicle-reidentification
There are 6 repositories under vehicle-reidentification topic.
bismex / Awesome-person-re-identification
Awesome Person Re-identification
CaptainEven / RepNet-MDNet-VehicleReID
Implementing RepNet(a two-stream multitask learning network) to do vehicle Re-identification, vehicle search(or vehicle match) with PyTorch 可用于车辆细粒度识别，车辆再识别，车辆匹配，车辆检索，RepNet/MDNet的一种PyTorch实现
Language:Python 240heshuting555 / AICITY2020_DMT_VehicleReID
The 3rd Place Submission to AICity Challenge 2020 Track2
Language:Python 210Zhongdao / VehicleReIDKeyPointData
Annotations of key point location and vehicle orientation for VeRi-776 dataset. ICCV'17 paper: Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification.
Language:Python 154yorkeyao / VehicleX
VehicleX: Simulating Content Consistent Vehicle Datasets with Attribute Descent (ECCV 2020, TPAMI 2023)
Language:Python 153Awesome Vehicle Re-identification
- Language:Python 107
icarofua / vehicle-rear
Vehicle-Rear: A New Dataset to Explore Feature Fusion For Vehicle Identification Using Convolutional Neural Networks
Language:Python 107924973292 / EDITOR
【CVPR2024】Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification
Language:Python 35zhangxinyu-xyz / PGAN-VehicleRe-ID
Code for Part-Guided Attention Learning for Vehicle Instance Retrieval (TITS2020). A strong Vehicle Re-ID model with part region guidance.
Language:Python 25tsaishien-chen / SPAN
Semantics-guided Part Attention Network (ECCV 2020 Oral)
Language:Python 22regob / vehicle_reid
Vehicle Re-identification
Language:Python 18Multi-modal Object Re-identification
- Language:Python 5
ramajoballester / UC3M-VRI
UC3M Vehicle Re-Identification dataset
sekilab / VehicleReIdentificationDataset
Vehicle Re-Identification (ReID) dataset contains over 55,000 images for training and validation of the vehicle re-identification model
Language:Jupyter Notebook 3Rubo12345 / Directed-Research-on-Vehicle-Re-Identification
In this project, a novel framework is used from the reference mentioned in the README, which successfully encodes both geometric local features and global representations to distinguish vehicle instances, optimized only by the supervision from official ID labels. Specifically, given the insight that objects in ReID share similar geometric characteristics, a self-supervised representation learning technique is used to facilitate geometric features discovery. To condense these features, an interpretable attention module, with the core of local maxima aggregation instead of fully automatic learning, in used, whose mechanism is completely understandable and whose response map is physically reasonable.
Language:Python 0
