1 citations · 1 across the 10 of their papers we have counts for
11 papers
How Far Are Video Models from True Multimodal Reasoning?
Xiaotian Zhang, Jianhui Wei, Yuan Wang +9
Despite remarkable progress toward general-purpose video models, a critical question remains unanswered: how far are these models from achieving true multimodal reasoning? Existing…
Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos
Songtao Jiang, Sibo Song, Chenyi Zhou +12
The transition from image to video understanding requires vision-language models (VLMs) to shift from recognizing static patterns to reasoning over temporal dynamics such as motion…
Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding
Songtao Jiang, Yuan Wang, Sibo Song +22
Real-world clinical decision-making requires integrating heterogeneous data, including medical text, 2D images, 3D volumes, and videos, while existing AI systems fail to unify all…
Knowing or Guessing? Robust Medical Visual Question Answering via Joint Consistency and Contrastive Learning
Songtao Jiang, Yuxi Chen, Sibo Song +5
In high-stakes medical applications, consistent answering across diverse question phrasings is essential for reliable diagnosis. However, we reveal that current Medical Vision-Lang…
Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning
Xiaotian Zhang, Yuan Wang, Zhaopeng Feng +6
Medical Question-Answering (QA) encompasses a broad spectrum of tasks, including multiple choice questions (MCQ), open-ended text generation, and complex computational reasoning. D…
CAPO: Reinforcing Consistent Reasoning in Medical Decision-Making
Songtao Jiang, Yuan Wang, Ruizhe Chen +8
In medical visual question answering (Med-VQA), achieving accurate responses relies on three critical steps: precise perception of medical imaging data, logical reasoning grounded…
Mitigating Posterior Salience Attenuation in Long-Context LLMs with Positional Contrastive Decoding
Zikai Xiao, Ziyang Wang, Wen Ma +5
While Large Language Models (LLMs) support long contexts, they struggle with performance degradation within the context window. Current solutions incur prohibitive training costs,…
Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering
Songtao Jiang, Chenyi Zhou, Yan Zhang +2
Multimodal large language models (MLLMs) still struggle with complex reasoning tasks in Visual Question Answering (VQA). While current methods have advanced by incorporating visual…
HSCR: Hierarchical Self-Contrastive Rewarding for Aligning Medical Vision Language Models
Songtao Jiang, Yan Zhang, Yeying Jin +5
Medical Vision-Language Models (Med-VLMs) have achieved success across various tasks, yet most existing methods overlook the modality misalignment issue that can lead to untrustwor…
OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding
Songtao Jiang, Yuan Wang, Sibo Song +6
The practical deployment of medical vision-language models (Med-VLMs) necessitates seamless integration of textual data with diverse visual modalities, including 2D/3D images and v…
Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language Models
Songtao Jiang, Yan Zhang, Chenyi Zhou +4
Multimodal Large Language Models (MLLMs) such as GPT-4V and Gemini Pro face challenges in achieving human-level perception in Visual Question Answering (VQA), particularly in objec…