Generalizable Person Re-identification
12 papers with code • 12 benchmarks • 6 datasets
Generalizable person re-identification refers to methods trained on a source dataset but directly evaluated on a target dataset without domain adaptation or transfer learning.
Benchmarks
These leaderboards are used to track progress in Generalizable Person Re-identification
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MSMT17
Market-1501-C
Most implemented papers
Semi-Supervised Domain Generalizable Person Re-Identification
Instead, we aim to explore multiple labeled datasets to learn generalized domain-invariant representations for person re-id, which is expected universally effective for each new-coming re-id scenario.
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting
shengcailiao/QAConv
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ECCV 2020
In this paper, beyond representation learning, we consider how to formulate person image matching directly in deep feature maps.
Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification
VideoObjectSearch/RandPerson
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To address this, we propose to automatically synthesize a large-scale person re-identification dataset following a set-up similar to real surveillance but with virtual environments, and then use the synthesized person images to train a generalizable person re-identification model.
Dual Distribution Alignment Network for Generalizable Person Re-Identification
Pealing/DDAN
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Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID), which trains the model using labels from the source domain alone, and then directly adopts the trained model to the target domain without model updating.
DomainMix: Learning Generalizable Person Re-Identification Without Human Annotations
In this way, human annotations are no longer required, and it is scalable to large and diverse real-world datasets.
Meta Batch-Instance Normalization for Generalizable Person Re-Identification
To this end, we combine learnable batch-instance normalization layers with meta-learning and investigate the challenging cases caused by both batch and instance normalization layers.
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification
shengcailiao/QAConv
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CVPR 2022
Though online hard example mining has improved the learning efficiency to some extent, the mining in mini batches after random sampling is still limited.
TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification
ShengcaiLiao/TransMatcher
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NeurIPS 2021
In this work, we further investigate the possibility of applying Transformers for image matching and metric learning given pairs of images.
Benchmarks for Corruption Invariant Person Re-identification
When deploying person re-identification (ReID) model in safety-critical applications, it is pivotal to understanding the robustness of the model against a diverse array of image corruptions.
Calibrated Feature Decomposition for Generalizable Person Re-Identification
The calibrated person representation is subtly decomposed into the identity-relevant feature, domain feature, and the remaining entangled one.
