Category Query Learning for Human-Object Interaction Classification

Autor:	Xie, Chi, Zeng, Fangao, Hu, Yue, Liang, Shuang, Wei, Yichen
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Unlike most previous HOI methods that focus on learning better human-object features, we propose a novel and complementary approach called category query learning. Such queries are explicitly associated to interaction categories, converted to image specific category representation via a transformer decoder, and learnt via an auxiliary image-level classification task. This idea is motivated by an earlier multi-label image classification method, but is for the first time applied for the challenging human-object interaction classification task. Our method is simple, general and effective. It is validated on three representative HOI baselines and achieves new state-of-the-art results on two benchmarks. Comment: Accepted by CVPR 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.14005 Zobrazit plný text záznamu View this record from Arxiv