79 citations · 190 across the 12 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…
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Yizhe Zhu, Jianwen Xie, Zhiqiang Tang +2
Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes. Existing approaches predominan…
CU-Net: Coupled U-Nets
Zhiqiang Tang, Xi Peng, Shijie Geng +2
We design a new connectivity pattern for the U-Net architecture. Given several stacked U-Nets, we couple each U-Net pair through the connections of their semantic blocks, resulting…
Quantized Densely Connected U-Nets for Efficient Landmark Localization
Zhiqiang Tang, Xi Peng, Shijie Geng +3
In this paper, we propose quantized densely connected U-Nets for efficient visual landmark localization. The idea is that features of the same semantic meanings are globally reused…
Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation
Xi Peng, Zhiqiang Tang, Fei Yang +2
Random data augmentation is a critical technique to avoid overfitting in training deep neural network models. However, data augmentation and network training are usually treated as…
Toward Marker-free 3D Pose Estimation in Lifting: A Deep Multi-view Solution
Rahil Mehrizi, Xi Peng, Zhiqiang Tang +3
Lifting is a common manual material handling task performed in the workplaces. It is considered as one of the main risk factors for Work-related Musculoskeletal Disorders. To impro…
Vision-based Robotic Arm Imitation by Human Gesture
Cheng Xuan, Zhiqiang Tang, Jinxin Xu
One of the most efficient ways for a learning-based robotic arm to learn to process complex tasks as human, is to directly learn from observing how human complete those tasks, and…