no code implementations • 24 Apr 2024 • Zhiqiang Tang, Haoyang Fang, Su Zhou, Taojiannan Yang, Zihan Zhong, Tony Hu, Katrin Kirchhoff, George Karypis
AutoGluon-Multimodal (AutoMM) is introduced as an open-source AutoML library designed specifically for multimodal learning.
1 code implementation • 31 Jan 2024 • Zihan Zhong, Zhiqiang Tang, Tong He, Haoyang Fang, Chun Yuan
The Segment Anything Model (SAM) stands as a foundational framework for image segmentation.
1 code implementation • 29 Dec 2022 • Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson
The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems.
no code implementations • 15 Dec 2022 • JieLin Qiu, Yi Zhu, Xingjian Shi, Florian Wenzel, Zhiqiang Tang, Ding Zhao, Bo Li, Mu Li
Multimodal image-text models have shown remarkable performance in the past few years.
no code implementations • 10 Oct 2022 • Yunhe Gao, Xingjian Shi, Yi Zhu, Hao Wang, Zhiqiang Tang, Xiong Zhou, Mu Li, Dimitris N. Metaxas
First, DePT plugs visual prompts into the vision Transformer and only tunes these source-initialized prompts during adaptation.
Ranked #4 on Domain Adaptation on VisDA2017
1 code implementation • 30 Mar 2021 • Yunhe Gao, Zhiqiang Tang, Mu Zhou, Dimitris Metaxas
Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance.
1 code implementation • ICCV 2021 • Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris Metaxas
Can we develop new normalization methods to improve generalization robustness under distribution shifts?
no code implementations • 1 Jan 2021 • Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris N. Metaxas
CrossNorm exchanges styles between feature channels to perform style augmentation, diversifying the content and style mixtures.
1 code implementation • ECCV 2020 • Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky, Prasanna Sattigeri, Rogerio Feris, Dimitris Metaxas
First is that most if not all modern augmentation search methods are offline and learning policies are isolated from their usage.
no code implementations • ICCV 2019 • Zhiqiang Tang, Xi Peng, Tingfeng Li, Yizhe Zhu, Dimitris N. Metaxas
The AdaTransform can increase data variance in training and decrease data variance in testing.
no code implementations • NeurIPS 2019 • Yizhe Zhu, Jianwen Xie, Zhiqiang Tang, Xi Peng, Ahmed Elgammal
Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes.
1 code implementation • 20 Aug 2018 • Zhiqiang Tang, Xi Peng, Shijie Geng, Yizhe Zhu, Dimitris N. Metaxas
We design a new connectivity pattern for the U-Net architecture.
Ranked #30 on Pose Estimation on MPII Human Pose
1 code implementation • ECCV 2018 • Zhiqiang Tang, Xi Peng, Shijie Geng, Lingfei Wu, Shaoting Zhang, Dimitris Metaxas
Finally, to reduce the memory consumption and high precision operations both in training and testing, we further quantize weights, inputs, and gradients of our localization network to low bit-width numbers.
Ranked #19 on Pose Estimation on MPII Human Pose
no code implementations • CVPR 2018 • Xi Peng, Zhiqiang Tang, Fei Yang, Rogerio Feris, Dimitris Metaxas
Random data augmentation is a critical technique to avoid overfitting in training deep neural network models.
Ranked #3 on Pose Estimation on Leeds Sports Poses
no code implementations • 6 Feb 2018 • Rahil Mehrizi, Xi Peng, Zhiqiang Tang, Xu Xu, Dimitris Metaxas, Kang Li
The results are also compared with state-of-the-art methods on HumanEva-I dataset, which demonstrates the superior performance of our approach.
no code implementations • 15 Mar 2017 • 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 then imitate.
Robotics