Visual Cue
Visual cues, encompassing visual information used for perception and decision-making, are a central focus in current research across diverse fields. Studies explore how visual cues are integrated with other modalities (like language or physiological signals) using various machine learning models, including large language models, graph neural networks, and transformer architectures, to improve tasks such as object recognition, human-computer interaction, and medical diagnosis. This research is significant because effectively leveraging visual cues enhances the robustness and accuracy of artificial intelligence systems in numerous applications, from autonomous driving to healthcare.
Papers
Visual Cue Enhancement and Dual Low-Rank Adaptation for Efficient Visual Instruction Fine-Tuning
Pengkun Jiao, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang
Neuro-3D: Towards 3D Visual Decoding from EEG Signals
Zhanqiang Guo, Jiamin Wu, Yonghao Song, Weijian Mai, Qihao Zheng, Wanli Ouyang, Chunfeng Song