3 citations · 3 across the 6 of their papers we have counts for
7 papers
OmniTraffic: A Controllable Generation Pipeline and Benchmark for Spatio-Temporal Traffic Reasoning
Maonan Wang, Zhengyan Huang, Kemou Jiang +13
Traffic scene understanding requires models to reason beyond object recognition, including lane topology, multi-view geometry, temporal evolution, and signal-phase semantics. Howev…
StoryLens: Preference-Aligned Story Rewriting via Context-Aware Narrative Enrichment
Hanwen Cui, Yuting Mei, Yuhang Fu +2
Story rewriting aims to adapt existing narratives to diverse reader preferences while preserving plot consistency and narrative coherence. Unlike conventional work on style transfe…
Towards a Comprehensive Scaling Law of Mixture-of-Experts
Guoliang Zhao, Yuhan Fu, Shuaipeng Li +10
Mixture-of-Experts (MoE) models have become the consensus approach for enabling parameter-efficient scaling and cost-effective deployment in large language models. However, existin…
Multi-Object Sketch Animation by Scene Decomposition and Motion Planning
Jingyu Liu, Zijie Xin, Yuhan Fu +3
Sketch animation, which brings static sketches to life by generating dynamic video sequences, has found widespread applications in GIF design, cartoon production, and daily enterta…
Mitigating Hallucination in Multimodal Large Language Model via Hallucination-targeted Direct Preference Optimization
Yuhan Fu, Ruobing Xie, Xingwu Sun +2
Multimodal Large Language Models (MLLMs) are known to hallucinate, which limits their practical applications. Recent works have attempted to apply Direct Preference Optimization (D…
Magnifier Prompt: Tackling Multimodal Hallucination via Extremely Simple Instructions
Yuhan Fu, Ruobing Xie, Jiazhen Liu +4
Hallucinations in multimodal large language models (MLLMs) hinder their practical applications. To address this, we propose a Magnifier Prompt (MagPrompt), a simple yet effective m…
PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset
Jiazhen Liu, Yuhan Fu, Ruobing Xie +5
Multimodal Large Language Models (MLLMs) hallucinate, resulting in an emerging topic of visual hallucination evaluation (VHE). This paper contributes a ChatGPT-Prompted visual hall…