Liang-Chieh (Jay) Chen- Home Page

Liang-Chieh Chen
Research Scientist, ByteDance, Los Angeles

Email: lcchen at cs dot ucla dot edu

About Me

I am currently a Research Scientist at ByteDance / TikTok with a focus on Computer Vision and Deep Learning.

I am best known for my contributions to the DeepLab series (co-developed with George Papandreou): DeepLabv1, DeepLabv2, DeepLabv3, DeepLabv3+. Since December 2014, our introduced atrous convolution (also known as convolution with holes or dilated convolution) has been widely used for dense prediction tasks.

Influential DeepLab derivatives include Auto-DeepLab, Panoptic-DeepLab (winning method of the Mapillary Vistas Panoptic Segmentation track at ICCV 2019, and top performer on Cityscapes leaderboards), Axial-DeepLab, ViP-DeepLab, MaX-DeepLab, and kMaX-DeepLab. Additionally, I am known for my collaborative work on MobileNetv2 and MobileNetv3, which have set new benchmarks in neural network design and search for mobile devices.

Before joining ByteDance, I spent 7 years as a Research Scientist at Google in Los Angeles from 2016 to 2023. In 2015, I earned my Ph.D. in Computer Science from UCLA, where I was advised by Alan L. Yuille.

News

For motivated Ph.D. students looking for internships, feel free to contact me for details. Our team works on fundamental research on visual recognition, representation learning, and deep learning.

Activities

Area Chair for ICCV 2019, CVPR 2020, 2023, 2024, 2025, ECCV 2020, 2024, NeurIPS 2022, 2024.

Reviewer for CVPR, ECCV, ICCV, NeurIPS.

Selected Recent Publications

MaskBit: Embedding-free Image Generation via Bit Tokens
Mark Weber, Lijun Yu, Qihang Yu, Xueqing Deng, Xiaohui Shen, Daniel Cremers, Liang-Chieh Chen
Technical report
[preprint (arxiv: 2409.16211)] [project website]

Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models
Qihao Liu*, Zhanpeng Zeng*, Ju He*, Qihang Yu, Xiaohui Shen, Liang-Chieh Chen
(*equal contribution)
In Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2024.
[preprint (arxiv: 2406.09416)] [project website] [code]

An Image is Worth 32 Tokens for Reconstruction and Generation
Qihang Yu*, Mark Weber*, Xueqing Deng, Xiaohui Shen, Daniel Cremers, Liang-Chieh Chen
(*equal contribution)
In Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2024.
[preprint (arxiv: 2406.07550)] [project website] [code]

Enhancing Temporal Consistency in Video Editing by Reconstructing Videos with 3D Gaussian Splatting
Inkyu Shin, Qihang Yu, Xiaohui Shen, In So Kweon, Kuk-Jin Yoon, Liang-Chieh Chen
Technical report
[preprint (arxiv: 2406.02541)] [project website]

COCONut: Modernizing COCO Segmentation
Xueqing Deng, Qihang Yu, Peng Wang, Xiaohui Shen, Liang-Chieh Chen
In Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, USA, June 2024.
[preprint (arxiv: 2404.08639)] [project website] [code] [data (Kaggle)]

ViTamin: Designing Scalable Vision Models in the Vision-Language Era
Jieneng Chen, Qihang Yu, Xiaohui Shen, Alan Yuille, Liang-Chieh Chen
In Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, USA, June 2024.
[preprint (arxiv: 2404.02132)] [project website] [code] [HuggingFace]

MaXTron: Mask Transformer with Trajectory Attention for Video Panoptic Segmentation
Ju He, Qihang Yu, Inkyu Shin, Xueqing Deng, Xiaohui Shen, Alan Yuille, Liang-Chieh Chen
Transactions on Machine Learning Research (TMLR), June 2024.
[preprint (arxiv: 2311.18537)] [PyTorch code]