1 code implementation • 8 May 2024 • Yibo Zhou, Hai-Miao Hu, Yirong Xiang, Xiaokang Zhang, Haotian Wu
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.
1 code implementation • 28 Jul 2023 • Yibo Zhou, Hai-Miao Hu, Jinzuo Yu, Zhenbo Xu, Weiqing Lu, Yuran Cao
Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurrence among attributes.
no code implementations • 16 May 2023 • Hai-Miao Hu, Zhenbo Xu, Wenshuai Xu, You Song, YiTao Zhang, Liu Liu, Zhilin Han, Ajin Meng
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.
no code implementations • 1 Sep 2017 • Xiaowei Zhang, Li Cheng, Bo Li, Hai-Miao Hu
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.