1 citations · 1 across the 12 of their papers we have counts for
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DenseAnnotate: Enabling Scalable Dense Caption Collection for Images and 3D Scenes via Spoken Descriptions
Xiaoyu Lin, Aniket Ghorpade, Hansheng Zhu +9
With the rapid adoption of multimodal large language models (MLLMs) across diverse applications, there is a pressing need for task-centered, high-quality training data. A key limit…
Explainable AI-Generated Image Detection RewardBench
Michael Yang, Shijian Deng, William T. Doan +4
Conventional, classification-based AI-generated image detection methods cannot explain why an image is considered real or AI-generated in a way a human expert would, which reduces…
Zoom-Refine: Boosting High-Resolution Multimodal Understanding via Localized Zoom and Self-Refinement
Xuan Yu, Dayan Guan, Yanfeng Gu
Multimodal Large Language Models (MLLM) often struggle to interpret high-resolution images accurately, where fine-grained details are crucial for complex visual understanding. We i…
DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning
Ziwei Zheng, Michael Yang, Jack Hong +5
Large Vision-Language Models excel at multimodal understanding but struggle to deeply integrate visual information into their predominantly text-based reasoning processes, a key ch…
Graph Relation Transformer: Incorporating pairwise object features into the Transformer architecture
Michael Yang, Aditya Anantharaman, Zachary Kitowski +1
Previous studies such as VizWiz find that Visual Question Answering (VQA) systems that can read and reason about text in images are useful in application areas such as assisting vi…