Human Understanding
Human understanding, a multifaceted field encompassing cognitive processes and AI model capabilities, seeks to unravel how humans and machines comprehend information. Current research focuses on improving AI's ability to understand nuanced language, visual information, and complex relationships within data, employing techniques like multimodal large language models, hypergraph attention networks, and retrieval-augmented generation. These advancements have implications for various applications, including improved medical diagnosis, enhanced human-computer interaction, and more effective scientific knowledge extraction, but challenges remain in achieving truly robust and generalizable understanding in AI.
Papers
SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE
Yongwei Chen, Yushi Lan, Shangchen Zhou, Tengfei Wang, XIngang Pan
SAVEn-Vid: Synergistic Audio-Visual Integration for Enhanced Understanding in Long Video Context
Jungang Li, Sicheng Tao, Yibo Yan, Xiaojie Gu, Haodong Xu, Xu Zheng, Yuanhuiyi Lyu, Linfeng Zhang, Xuming Hu
ULTra: Unveiling Latent Token Interpretability in Transformer Based Understanding
Hesam Hosseini, Ghazal Hosseini Mighan, Amirabbas Afzali, Sajjad Amini, Amir Houmansadr
Towards Utilising a Range of Neural Activations for Comprehending Representational Associations
Laura O'Mahony, Nikola S. Nikolov, David JP O'Sullivan
Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level
Andong Deng, Tongjia Chen, Shoubin Yu, Taojiannan Yang, Lincoln Spencer, Yapeng Tian, Ajmal Saeed Mian, Mohit Bansal, Chen Chen