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Found 5 papers, 1 papers with code
Weakly Supervised Learning with Side Information for Noisy Labeled Images
In many real-world datasets, like WebVision, the performance of DNN based classifier is often limited by the noisy labeled data.
Adversarially Occluded Samples for Person Re-Identification
Person re-identification (ReID) is the task of retrieving particular persons across different cameras.
Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification
It is a challenging task due to the large variations in person pose, occlusion, background clutter, etc How to extract powerful features is a fundamental problem in ReID and is still an open problem today.
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Person Re-Identification
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Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization
Based on GoogLeNet, firstly, a set of mid-level attribute features are discovered by novelly designed detection layers, where a max-pooling based weakly-supervised object detection technique is used to train these layers with only image-level labels without the need of bounding box annotations of pedestrian attributes.
A Richly Annotated Dataset for Pedestrian Attribute Recognition
RAP has in total 41, 585 pedestrian samples, each of which is annotated with 72 attributes as well as viewpoints, occlusions, body parts information.
