Generalizable Person Re-identification
21 papers with code • 4 benchmarks • 9 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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Market-1501
CUHK03
MSMT17
Market-1501-C
ClonedPerson
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.
TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification
shengcailiao/QAConv
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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.
Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-Identification
yanan-wang-cs/clonedperson
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CVPR 2022
To address this, in this work, an automatic approach is proposed to directly clone the whole outfits from real-world person images to virtual 3D characters, such that any virtual person thus created will appear very similar to its real-world counterpart.
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.
Style Normalization and Restitution for Generalizable Person Re-identification
Existing fully-supervised person re-identification (ReID) methods usually suffer from poor generalization capability caused by domain gaps.
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.
