Recent Advances in Meta and Self-Supervised Learning for Visual Analysis
- 2022-03-11 (Fri.), 10:30 AM
- Auditorium, B1F, Institute of Statistical Science；The tea reception will be held at 10:10.
- Lecture in Mandarin. Online live streaming through Microsoft Teams will be available.
- Prof. Yu-Chiang Frank Wang
- Department of Electrical Engineering & Graduate Institute of Communication Engineering, National Taiwan University
Abstract
Deep learning has shown its success in various tasks in areas such as computer vision and natural language processing.
However, while deep learning models trained in fully-supervised fashions have exhibited promising performances, how to design and train such models with small or unsupervised data remains a challenging task. In this talk, I will go over recent trends for deep learning (particularly in computer vision) on the development of meta and self-supervised learning strategies, which can be applied to tackle the aforementioned challenging yet practical settings. I will also talk about our recent NeurIPS, CVPR, and ECCV works, and explain how we advance these learning schemes for visual analysis.
Please click here for participating the talk online
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