Search Results for author:
Found 8 papers, 4 papers with code
MuMA-ToM: Multi-modal Multi-Agent Theory of Mind
To truly understand how and why people interact with one another, we must infer the underlying mental states that give rise to the social interactions, i. e., Theory of Mind reasoning in multi-agent interactions.
JieHua Paintings Style Feature Extracting Model using Stable Diffusion with ControlNet
This study proposes a novel approach to extract stylistic features of Jiehua: the utilization of the Fine-tuned Stable Diffusion Model with ControlNet (FSDMC) to refine depiction techniques from artists' Jiehua.
A New Chinese Landscape Paintings Generation Model based on Stable Diffusion using DreamBooth
This study mainly introduces a method combining the Stable Diffusion Model (SDM) and Parameter-Efficient Fine-Tuning method for generating Chinese Landscape Paintings.
VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models
Based on the evaluation results obtained with the toolkit, we host OpenVLM Leaderboard, a comprehensive leaderboard to track the progress of multi-modality learning research.
Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs
Vision Language Models (VLMs) demonstrate remarkable proficiency in addressing a wide array of visual questions, which requires strong perception and reasoning faculties.
MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding
The advent of large vision-language models (LVLMs) has spurred research into their applications in multi-modal contexts, particularly in video understanding.
LoRA-Switch: Boosting the Efficiency of Dynamic LLM Adapters via System-Algorithm Co-design
Recent literature has found that an effective method to customize or further improve large language models (LLMs) is to add dynamic adapters, such as low-rank adapters (LoRA) with Mixture-of-Experts (MoE) structures.
Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM
While there has been a lot of research on representation learning of structured EHR data, the fusion of different types of EHR data (multimodal fusion) is not well studied.
