most citedFrequency Autoregressive Image Generation with Continuous Tokens

1 citations · 1 across the 12 of their papers we have counts for

collaborators

12 papers

cs.CV2026

ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding

Hangjie Yuan, Yichen Qian, Zhiwei Tang +21

Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric chall…

cs.CV2026

Towards Error-Free Long Video Generation

Shuning Chang, Weihua Chen, Jiasheng Tang +8

Recent advances in video generation have made minute-level synthesis possible; however, generating long videos remains challenging due to error accumulation, attribute drift, and t…

cs.CV2026

ClinHallu: A Benchmark for Diagnosing Stage-Wise Hallucinations in Medical MLLM Reasoning

Sicheng Yang, Hangjie Yuan, Wenjun Zhang +5

Building trustworthy medical multimodal large language models (MLLMs) is critical for reliable clinical decision support. Existing medical hallucination benchmarks mainly focus on…

cs.CV2026

Towards 3D-Aware Video Diffusion Models: Render-Free Human Motion Control with Mesh Tokenization

Jingyun Liang, Min Wei, Shikai Li +5

Diffusion models have shown remarkable success in video generation. However, whether such models are truly aware of the 3D structure underlying visual observations, rather than sim…

cs.CV2026

Knowledge is Power: Advancing Few-shot Action Recognition with Multimodal Semantics from MLLMs

Jiazheng Xing, Chao Xu, Hangjie Yuan +4

Multimodal Large Language Models (MLLMs) have propelled the field of few-shot action recognition (FSAR). However, preliminary explorations in this area primarily focus on generatin…

cs.CV2026

MCIE: Multimodal LLM-Driven Complex Instruction Image Editing with Spatial Guidance

Xuehai Bai, Xiaoling Gu, Akide Liu +3

Recent advances in instruction-based image editing have shown remarkable progress. However, existing methods remain limited to relatively simple editing operations, hindering real-…