6 papers
Video Generation Models: A Survey of Post-Training and Alignment
Chaoyu Li, Xiaoyi Gu, Yogesh Kulkarni +10
Video generation has rapidly progressed from short, low-quality clips to high-resolution, long-duration sequences with complex spatiotemporal dynamics. Despite strong generative pr…
CASHEW: Stabilizing Multimodal Reasoning via Iterative Trajectory Aggregation
Chaoyu Li, Fei Tao, Pooyan Fazli
Vision-language models achieve strong performance across a wide range of multimodal understanding and reasoning tasks, yet their multi-step reasoning remains unstable. Repeated sam…
FrameOracle: Learning What to See and How Much to See in Videos
Chaoyu Li, Tianzhi Li, Fei Tao +6
Vision-language models (VLMs) advance video understanding but operate under tight computational budgets, making performance dependent on selecting a small, high-quality subset of f…
ReGATE: Learning Faster and Better with Fewer Tokens in MLLMs
Chaoyu Li, Yogesh Kulkarni, Pooyan Fazli
The computational cost of training multimodal large language models (MLLMs) grows rapidly with the number of processed tokens. Existing efficiency methods mainly target inference v…
VideoA11y: Method and Dataset for Accessible Video Description
Chaoyu Li, Sid Padmanabhuni, Maryam Cheema +2
Video descriptions are crucial for blind and low vision (BLV) users to access visual content. However, current artificial intelligence models for generating descriptions often fall…
VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding
Chaoyu Li, Eun Woo Im, Pooyan Fazli
Multimodal large language models (MLLMs) have recently shown significant advancements in video understanding, excelling in content reasoning and instruction-following tasks. Howeve…