1 citations · 1 across the 5 of their papers we have counts for
6 papers
In-Context Operator Learning on the Space of Probability Measures
Frank Cole, Dixi Wang, Yineng Chen +2
We introduce \emph{in-context operator learning on probability measure spaces} for optimal transport (OT). The goal is to learn a single solution operator that maps a pair of distr…
SAGE: Spuriousness-Aware Guided Prompt Exploration for Mitigating Multimodal Bias
Wenqian Ye, Di Wang, Guangtao Zheng +2
Large vision-language models, such as CLIP, have shown strong zero-shot classification performance by aligning images and text in a shared embedding space. However, CLIP models oft…
Collaborative Text-to-Image Generation via Multi-Agent Reinforcement Learning and Semantic Fusion
Jiabao Shi, Minfeng Qi, Lefeng Zhang +5
Multimodal text-to-image generation remains constrained by the difficulty of maintaining semantic alignment and professional-level detail across diverse visual domains. We propose…
ARC-Hunyuan-Video-7B: Structured Video Comprehension of Real-World Shorts
Yuying Ge, Yixiao Ge, Chen Li +15
Real-world user-generated short videos, especially those distributed on platforms such as WeChat Channel and TikTok, dominate the mobile internet. However, current large multimodal…
Learning-to-Context Slope: Evaluating In-Context Learning Effectiveness Beyond Performance Illusions
Dingzriui Wang, Xuanliang Zhang, Keyan Xu +3
In-context learning (ICL) has emerged as an effective approach to enhance the performance of large language models (LLMs). However, its effectiveness varies significantly across mo…
CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information
Kaifan Zhang, Lihuo He, Xin Jiang +3
Electroencephalogram (EEG) signals have attracted significant attention from researchers due to their non-invasive nature and high temporal sensitivity in decoding visual stimuli.…