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Found 4 papers, 2 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.
One-shot neural band selection for spectral recovery
To solve this ill-posed inverse problem, most band selection methods adopt hand-crafted priors or exploit clustering or sparse regularization constraints to find most prominent bands.
Too Far to See? Not Really! --- Pedestrian Detection with Scale-aware Localization Policy
A major bottleneck of pedestrian detection lies on the sharp performance deterioration in the presence of small-size pedestrians that are relatively far from the camera.
