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
Latest papers with no code
Generalizable Multi-Camera 3D Pedestrian Detection
We present a multi-camera 3D pedestrian detection method that does not need to train using data from the target scene.
Learning Domain Invariant Representations for Generalizable Person Re-Identification
Generalizable person Re-Identification (ReID) has attracted growing attention in recent computer vision community.
Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning
That is, while a number of labeled source-domain datasets are available, we do not have access to any target-domain training data.
Augmented Hard Example Mining for Generalizable Person Re-Identification
Although the performance of person re-identification (Re-ID) has been much improved by using sophisticated training methods and large-scale labelled datasets, many existing methods make the impractical assumption that information of a target domain can be utilized during training.
Generalizable Person Re-Identification by Domain-Invariant Mapping Network
We aim to learn a domain generalizable person re-identification (ReID) model.
