Jeff Liang

Feng (Jeff) Liang, 梁丰

I am a PhD student at UT Austin, fortunately working with Prof. Diana Marculescu. I obtained my master and bachelor degree from Tsinghua University and Huazhong University of Science and Technology, respectively.

My current research interests lie in efficient machine learning, multimodal learning as well as their applications. If you find any research interests that we might share, feel free to drop me an email. I am always open to potential collaborations.

Email / CV / Google Scholar / Linkedin / Zhihu / Twitter

News

February 2023: One paper gets accepted to CVPR 2023!
November 2022: Checkout our Open-vocabulary Segmentation (OVSeg) with codes and demo!
August 2022: Checkout our Supervised MAE (SupMAE) with codes&models!
June 2022: Three papers get accepted to ICML workshops 2022!
April 2022: One paper gets accepted to IJCAI 2022 as long oral!
March 2022: One paper gets accepted to CVPRW ECV 2022!
February 2022: I will intern at Meta Reality Labs this summer, fortunate to work with Dr. Bichen Wu!
January 2022: One paper gets accepted to ICLR 2022!
October 2021: Checkout our Data efficient CLIP (DeCLIP) with codes&models!
July 2021: One paper gets accepted to ICCV 2021!
April 2021: I am granted UT Austin Engineering Fellowship!

Publications

	Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP Feng Liang, Bichen Wu, Xiaoliang Dai, Kunpeng Li, Yinan Zhao, Hang Zhang, Peizhao Zhang, Peter Vajda, Diana Marculescu, CVPR, 2023 project page, arxiv, code, Huggingface demo For the first time, we show open-vocabulary generalist models match the performance of supervised specialist models without dataset-specific adaptations.
	Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang Jing Shao, Fengwei Yu, Junjie Yan ICLR*, 2022 arxiv, bibtex, code, video presentation We propose Data efficient CLIP (DeCLIP), a method to efficiently train CLIP via utilizing the widespread supervision among the image-text data.
	ANT: Adapt Network Across Time for Efficient Video Processing Feng Liang, Ting-Wu Chin, Yang Zhou, Diana Marculescu CVPRW ECV, 2022 arxiv, bibtex, we propose the ANT framework to harness these redundancies for reducing the computational cost of video processing. The proposed ANT adapts a purpose-fit network by inspecting the semantic differences between frames.
	RePre: Improving Self-Supervised Vision Transformer with Reconstructive Pre-training Luya Wang, Feng Liang, Yangguang Li, Honggang Zhang, Wanli Ouyang, Jing Shao IJCAI, 2022, Long oral arxiv, bibtex, We propose RePre to extends contrastive frameworks by adding a branch for reconstructing raw image pixels in parallel with the existing contrastive objective.
	Computation Reallocation for Object Detection Feng Liang, Chen Lin, Ronghao Guo, Ming Sun, Wei Wu, Junjie Yan, Wanli Ouyang ICLR, 2020 arXiv, bibtex We present CRNAS that can learn computation reallocation strategies across different feature resolution and spatial position diectly on the target detection dataset.
	Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search Mingzhu Shen, Feng Liang, Ruihao Gong, Yuhang Li, Chuming Li, Chen Lin, Fengwei Yu, Junjie Yan, Wanli Ouyang ICCV, 2021 arxiv, bibtex, code We present Once Quantization-Aware Training (OQAT), a novel framework that searches for quantized efficient models and deploys their quantized weights at the same time without additional post-process.
	Inception Convolution with Efficient Dilation Search Jie Liu, Chuming Li, Feng Liang, Chen Lin, Junjie Yan, Wanli Ouyang, Dong Xu CVPR, 2021, Oral arxiv, bibtex, code We proposed a new mutant of dilated convolution, namely inception (dilated) convolution where the convolutions have independent dilation among different axes, channels and layers.
	Fully Quantized Network for Object Detection Rundong Li, Yan Wang, Feng Liang, Hongwei Qin, Junjie Yan, Rui Fan CVPR, 2019 CVF, bibtex, code We apply our techniques to produce fully quantized 4-bit detectors based on RetinaNet and Faster RCNN, and show that these achieve state-of-the-art performance for quantized detectors.
	NASGEM: Neural Architecture Search via Graph Embedding Method Hsin-Pai Cheng, Tunhou Zhang, Yixing Zhang, Shiyu Li, Feng Liang, Feng Yan, Meng Li, Vikas Chandra, Hai Li, Yiran Chen AAAI, 2021 arxiv, bibtex, We propose NASGEM which stands for Neural Architecture Search via Graph Embedding Method. NASGEM is driven by a novel graph embedding method equipped with similarity measures to capture the graph topology information.
	ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition Hsin-Pai Cheng, Feng Liang, Meng Li, Bowen Cheng, Feng Yan, Hai Li, Vikas Chandra, Yiran Chen AutoML-Conf, 2022 arxiv, bibtex We present ScaleNAS, a one-shot learning method for exploring scale-aware representations. Scale-NAS solves multiple tasks at a time by searching multi-scalefeature aggregation.