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Found 3 papers, 3 papers with code
Pedestrian Attribute Recognition as Label-balanced Multi-label Learning
Rooting in the scarcity of most attributes, realistic pedestrian attribute datasets exhibit unduly skewed data distribution, from which two types of model failures are delivered: (1) label imbalance: model predictions lean greatly towards the side of majority labels; (2) semantics imbalance: model is easily overfitted on the under-represented attributes due to their insufficient semantic diversity.
A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition
Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurrence among attributes.
Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection
In some scenarios, classifier requires detecting out-of-distribution samples far from its training data.
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