Back to Search
Start Over
HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining
- Publication Year :
- 2023
- Publisher :
- arXiv, 2023.
-
Abstract
- Human-centric perceptions include a variety of vision tasks, which have widespread industrial applications, including surveillance, autonomous driving, and the metaverse. It is desirable to have a general pretrain model for versatile human-centric downstream tasks. This paper forges ahead along this path from the aspects of both benchmark and pretraining methods. Specifically, we propose a \textbf{HumanBench} based on existing datasets to comprehensively evaluate on the common ground the generalization abilities of different pretraining methods on 19 datasets from 6 diverse downstream tasks, including person ReID, pose estimation, human parsing, pedestrian attribute recognition, pedestrian detection, and crowd counting. To learn both coarse-grained and fine-grained knowledge in human bodies, we further propose a \textbf{P}rojector \textbf{A}ssis\textbf{T}ed \textbf{H}ierarchical pretraining method (\textbf{PATH}) to learn diverse knowledge at different granularity levels. Comprehensive evaluations on HumanBench show that our PATH achieves new state-of-the-art results on 17 downstream datasets and on-par results on the other 2 datasets. The code will be publicly at \href{https://github.com/OpenGVLab/HumanBench}{https://github.com/OpenGVLab/HumanBench}.<br />Comment: Accepted to CVPR2023
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....cb12d05a6d6a7190562026b9d1c49a5e
- Full Text :
- https://doi.org/10.48550/arxiv.2303.05675