Beyond Part Models: Person Retrieval with Refined Part Pooling
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Updated
Sep 19, 2018 - Python
Beyond Part Models: Person Retrieval with Refined Part Pooling
Demo code for visible thermal (cross-modality) person re-identification
A Pytorch Implementation of "Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro"(ICCV17)
[CVPR2019, Oral] Learning Context Graph for Person Search
A memory balanced and communication efficient FullyConnected layer with CrossEntropyLoss model parallel implementation in PyTorch
Temporal Lifting (TLift), a model-free temporal cooccurrence based score weighting method proposed in "Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting".
open-reid with PCB, IDE, triplet, ZJU; MOT/MTMCT feature extraction support included
[CVPR2020] Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification
A multi-camera vehicle detection, tracking and re-identification system
A multi-camera vehicle detection, tracking and re-identification system
Code for "Space-Time Guided Association Learning For Unsupervised Person Re-Identification"
[CVPR2021] Meta Batch-Instance Normalization for Generalizable Person Re-Identification
Person re-identification MGN model based on tensorflow
Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification
[NeurIPS 2021] TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification
Vehicle Re-identification
[ECCV 2020] QAConv: Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting, and [CVPR 2022] GS: Graph Sampling Based Deep Metric Learning
TNNLS'22 🗽 Parameter-Efficient Person Re-identification in the 3D Space 🗽
[IEEE TIP under review] TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse Scenes
PyTorch code for ECCV 2020 paper: "Robust Re-Identification by Multiple Views Knowledge Distillation"
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