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

profile photo
News
  • 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
declip 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 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 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.

crnas 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.

oqa 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.

fqn 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.

fqn 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 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 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
Manuscript.
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.

Selected Honors
  • UT Austin Engineering Fellowship by UT Austin, 2021.
  • Excellent Student Leader by Tsinghua University, 2018.
  • National Scholarship by Ministry of Education of China, 2014 & 2015.
Service

Review papers for: IEEE TNNLS; CVPR ECV 2022


Thanks to Jon Barron