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20232026
most citedExploring the Distinctiveness and Fidelity of the Descriptions Generated by Large Vision-Language Models

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Alignment Dynamics in LLM Fine-Tuning

Yuhan Huang, Huanran Chen, Yinpeng Dong

Although Large Language Models (LLMs) achieve strong alignment through supervised fine-tuning and reinforcement learning from human feedback, the alignment is often fragile under s…

cs.CV2026

Building a Precise Video Language with Human-AI Oversight

Zhiqiu Lin, Chancharik Mitra, Siyuan Cen +13

Video-language models (VLMs) learn to reason about the dynamic visual world through natural language. We introduce a suite of open datasets, benchmarks, and recipes for scalable ov…

cs.CV2025

Towards Understanding Camera Motions in Any Video

Zhiqiu Lin, Siyuan Cen, Daniel Jiang +12

We introduce CameraBench, a large-scale dataset and benchmark designed to assess and improve camera motion understanding. CameraBench consists of ~3,000 diverse internet videos, an…

cs.CV2024★ 1 cited

Exploring the Distinctiveness and Fidelity of the Descriptions Generated by Large Vision-Language Models

Yuhang Huang, Zihan Wu, Chongyang Gao +2

Large Vision-Language Models (LVLMs) are gaining traction for their remarkable ability to process and integrate visual and textual data. Despite their popularity, the capacity of L…

cs.CV2023★ 1 cited

Manipulating the Label Space for In-Context Classification

Haokun Chen, Xu Yang, Yuhang Huang +3

After pre-training by generating the next word conditional on previous words, the Language Model (LM) acquires the ability of In-Context Learning (ICL) that can learn a new task co…