Feng (Jeff) Liang, 梁丰

I obtained my PhD from UT Austin, fortunately working with Prof. Diana Marculescu. Prior to that, I completed my master's at Tsinghua University and my bachelor's at Huazhong University of Science and Technology.

My current research interests lie in generative AI, especially video generation. If you find any research interests that we might share, feel free to drop me an email. I am always open to potential collaborations.

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We present Movie Weaver to support multi-concept video personalization.

We present StreamV2V to support real-time video-to-video translation for streaming input.

We leverage the temporal optical flow clue within video to enhance the temporal consistency for text guided video-to-video synthesis.

For the first time, we show open-vocabulary generalist models match the performance of supervised specialist models without dataset-specific adaptations.

SupMAE extends MAE to a fully-supervised setting by adding a supervised classification branch, thereby enabling MAE to effectively learn global features from golden labels.

We propose Data efficient CLIP (DeCLIP), a method to efficiently train CLIP via utilizing the widespread supervision among the image-text data.

• MLCommons ML and Systems Rising Stars by MLCommons 2024.
• Qualcomm Innovation Fellowship Finalist by Qualcomm 2024.
• UT Austin Engineering Fellowship by UT Austin, 2021 & 2023.
• Excellent Student Leader by Tsinghua University, 2018.
• National Scholarship by Ministry of Education of China, 2014 & 2015.

Thanks to Jon Barron