activity
20242026
collaborators

9 papers

cs.CV2026

Hierarchical Visual Agent: Managing Contexts in Joint Image-Text Space for Advanced Chart Reasoning

Qihua Dong, Ruozhen He, Junwen Chen +4

Advanced chart question answering requires both precise perception of small visual elements and multi-step reasoning across several subplots. While existing MLLMs are strong at und…

cs.CV2026

Distorted or Fabricated? A Survey on Hallucination in Video LLMs

Yiyang Huang, Yitian Zhang, Yizhou Wang +4

Despite significant progress in video-language modeling, hallucinations remain a persistent challenge in Video Large Language Models (Vid-LLMs), referring to outputs that appear pl…

cs.CL2026

Boosting Large Language Models with Mask Fine-Tuning

Mingyuan Zhang, Yue Bai, Huan Wang +4

The large language model (LLM) is typically integrated into the mainstream optimization protocol. No work has questioned whether maintaining the model integrity is \textit{indispen…

cs.CV2026

Ref-Adv: Exploring MLLM Visual Reasoning in Referring Expression Tasks

Qihua Dong, Kuo Yang, Lin Ju +6

Referring Expression Comprehension (REC) links language to region level visual perception. Standard benchmarks (RefCOCO, RefCOCO+, RefCOCOg) have progressed rapidly with multimodal…

cs.CV2025

D-CoDe: Scaling Image-Pretrained VLMs to Video via Dynamic Compression and Question Decomposition

Yiyang Huang, Yizhou Wang, Yun Fu

Video large language models (Vid-LLMs), which excel in diverse video-language tasks, can be effectively constructed by adapting image-pretrained vision-language models (VLMs). Howe…

cs.AI2025

Representation Potentials of Foundation Models for Multimodal Alignment: A Survey

Jianglin Lu, Hailing Wang, Yi Xu +3

Foundation models learn highly transferable representations through large-scale pretraining on diverse data. An increasing body of research indicates that these representations exh…