Ce Zhang (张册)

I am a first year Ph.D. student at UNC-Chapel Hill working with Prof. Gedas Bertasius. Previously, I obtained my Master's degree from Brown Universiy advised by Prof. Chen Sun in 2023. Before that, I obtained my Bachelor's degree from Southeast University in China in 2020.

I like music, sports, PC games, and watching funny videos.

Email  /  CV  /  Google Scholar  /  Twitter  /  Github

profile photo


I'm broadly interested in Computer Vision, Multimodal learning and Robotics. Currently, I'm mainly working on video understanding, with a focus on leveraging foundation models (LLMs, VLMs, etc.) to solve multiple video understanding tasks. I'm also interested in offline decision making, especially learning from videos. I believe that videos can provide rich sources of demonstrations for robot learning, and that the commonsense knowledge encoded in foundation models would help solve robotic tasks faster and more robustly.

clean-usnob A Simple LLM Framework for Long-Range Video Question-Answering
Ce Zhang*, Taixi Lu*, Md Mohaiminul Islam, Ziyang Wang, Shoubin Yu, Mohit Bansal, Gedas Bertasius
arXiv, 2023

We present LLoVi, a language-based framework for long-range video question-answering (LVQA). LLoVi decomposes LVQA into two stages: (1) visual captioning by a short-term visual captioner, and (2) long-range temporal reasoning by an LLM. We did thorough empirical analysis on our proposed framework. LLoVi achieves state-of-the-art performance on EgoSchema, NExT-QA, IntentQA and NExT-GQA.

clean-usnob AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?
Qi Zhao*, Shijie Wang*, Ce Zhang, Changcheng Fu, Minh Quan Do, Nakul Agarwal, Kwonjoon Lee, Chen Sun
ICLR, 2024

We use discretized action labels to represent videos, then feed the text representations to LLMs for long-term action anticipation. Results on Ego4D, EK-55 and Gaze show that this simple approach is suprisingly effective.

clean-usnob Object-centric Video Representation for Long-term Action Anticipation
Ce Zhang*, Changcheng Fu*, Shijie Wang, Nakul Agarwal, Kwonjoon Lee, Chiho Choi, Chen Sun
WACV, 2024

We proposed ObjectPrompts, an approach to extract task-specific object-centric representations from general-purpose pretrained models without finetuning. We also proposed a Transformer-based architecture to retrieve relevant objects from the past observation for long-term action anticipation.

clean-usnob Goal-Conditioned Predictive Coding as an Implicit Planner for Offline Reinforcement Learning
Zilai Zeng, Ce Zhang, Shijie Wang, Chen Sun
NeurIPS, 2023

We investigate if sequence modeling has the capability to condense trajectories into useful representations that can contribute to policy learning. GCPC achieves competitive performance on AntMaze, FrankaKitchen and Locomotion.


Reviewer: ECCV 2024, T4V @ CVPR 2024
Organizer: T4V @ CVPR 2024

This webpage is adapted from Jon Barron's page.