22 citations · 44 across the 5 of their papers we have counts for
12 papers
Learning Multimodal Data Augmentation in Feature Space
Zichang Liu, Zhiqiang Tang, Xingjian Shi +4
The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems. While there have been promising advances…
Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift
Jielin Qiu, Yi Zhu, Xingjian Shi +5
Multimodal image-text models have shown remarkable performance in the past few years. However, evaluating robustness against distribution shifts is crucial before adopting them in…
Visual Prompt Tuning for Test-time Domain Adaptation
Yunhe Gao, Xingjian Shi, Yi Zhu +5
Models should be able to adapt to unseen data during test-time to avoid performance drops caused by inevitable distribution shifts in real-world deployment scenarios. In this work,…
Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training
Yunhe Gao, Zhiqiang Tang, Mu Zhou +1
Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance. In medical…
CrossNorm and SelfNorm for Generalization under Distribution Shifts
Zhiqiang Tang, Yunhe Gao, Yi Zhu +3
Traditional normalization techniques (e.g., Batch Normalization and Instance Normalization) generally and simplistically assume that training and test data follow the same distribu…
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky +3
Data augmentation is one of the most important tools in training modern deep neural networks. Recently, great advances have been made in searching for optimal augmentation policies…