4 citations · 9 across the 9 of their papers we have counts for
10 papers
From Symbolic Perception to Logical Deduction: A Framework for Guiding Language Models in Geometric Reasoning
Weichen Dai, Rafael Medeiros Cabral, Ziyi Shou +4
Plane geometry remains a significant challenge in AI, requiring the integration of visual perception and mathematical reasoning. While Large Multimodal Models (LMMs) naturally hand…
MedSyn2: Flexible Control of 3D CT Generation via Text and Semantically-Defined Segmentation Prompts
Weicheng Dai, Chenyu Wang, Binxu Li +4
Generative models for volumetric medical images have found many applications in medical imaging, ranging from data augmentation to serving as priors for inverse problems. For these…
Enhancing Fine-Grained Spatial Grounding in 3D CT Report Generation via Discriminative Guidance
Chenyu Wang, Weicheng Dai, Han Liu +2
Vision--language models (VLMs) for radiology report generation (RRG) can produce long-form chest CT reports from volumetric scans and show strong potential to improve radiology wor…
Mammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reporting
Shantanu Ghosh, Vedant Parthesh Joshi, Rayan Syed +14
Breast cancer is one of the leading causes of death among women worldwide. We introduce Mammo-FM, the first foundation model specifically for mammography, pretrained on the largest…
RLDBF: Enhancing LLMs Via Reinforcement Learning With DataBase FeedBack
Weichen Dai, Zijie Dai, Zhijie Huang +6
While current large language models (LLMs) demonstrate remarkable linguistic capabilities through training on massive unstructured text corpora, they remain inadequate in leveragin…
KALE-LM-Chem: Vision and Practice Toward an AI Brain for Chemistry
Weichen Dai, Yezeng Chen, Zijie Dai +9
Recent advancements in large language models (LLMs) have demonstrated strong potential for enabling domain-specific intelligence. In this work, we present our vision for building a…
Exploring The Neural Burden In Pruned Models: An Insight Inspired By Neuroscience
Zeyu Wang, Weichen Dai, Xiangyu Zhou +2
Vision Transformer and its variants have been adopted in many visual tasks due to their powerful capabilities, which also bring significant challenges in computation and storage. C…
COEFF-KANs: A Paradigm to Address the Electrolyte Field with KANs
Xinhe Li, Zhuoying Feng, Yezeng Chen +4
To reduce the experimental validation workload for chemical researchers and accelerate the design and optimization of high-energy-density lithium metal batteries, we aim to leverag…
Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations
Chenyu You, Yifei Min, Weicheng Dai +3
Fine-tuning pre-trained vision-language models, like CLIP, has yielded success on diverse downstream tasks. However, several pain points persist for this paradigm: (i) directly tun…
MInD: Improving Multimodal Sentiment Analysis via Multimodal Information Disentanglement
Weichen Dai, Xingyu Li, Zeyu Wang +4
Learning effective joint representations has been a central task in multi-modal sentiment analysis. Previous works addressing this task focus on exploring sophisticated fusion tech…