10 citations · 15 across the 11 of their papers we have counts for
12 papers
RAGCell: Retrieval-Augmented Generation as Supervision for Versatile Single-cell Analysis
Tianyu Liu, Fan Zhang, Jiayuan Chen +10
Single-cell foundation models (scFMs) are transforming computational biology by enabling generalizable, task-agnostic representations for versatile single-cell analysis. Despite th…
Rethinking Facial Expression Recognition in the Era of Multimodal Large Language Models: Benchmark, Datasets, and Beyond
Fan Zhang, Haoxuan Li, Shengju Qian +9
Multimodal Large Language Models (MLLMs) have revolutionized numerous research fields, including computer vision and affective computing. As a pivotal challenge in this interdiscip…
Spatiotemporal Forecasting as Planning: A Model-Based Reinforcement Learning Approach with Generative World Models
Hao Wu, Yuan Gao, Xingjian Shi +9
To address the dual challenges of inherent stochasticity and non-differentiable metrics in physical spatiotemporal forecasting, we propose Spatiotemporal Forecasting as Planning (S…
MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models
Fan Zhang, Zebang Cheng, Chong Deng +18
Recent advances in multimodal large language models (MLLMs) have catalyzed transformative progress in affective computing, enabling models to exhibit emergent emotional intelligenc…
CellVerse: Do Large Language Models Really Understand Cell Biology?
Fan Zhang, Tianyu Liu, Zhihong Zhu +7
Recent studies have demonstrated the feasibility of modeling single-cell data as natural languages and the potential of leveraging powerful large language models (LLMs) for underst…
Turb-L1: Achieving Long-term Turbulence Tracing By Tackling Spectral Bias
Hao Wu, Yuan Gao, Chang Liu +11
Accurately predicting the long-term evolution of turbulence is crucial for advancing scientific understanding and optimizing engineering applications. However, existing deep learni…