SHAPE: An Unified Approach to Evaluate the Contribution and Cooperation of Individual Modalities

Kavli Affiliate: Yi Zhou | First 5 Authors: Pengbo Hu, Xingyu Li, Yi Zhou, , | Summary: As deep learning advances, there is an ever-growing demand for models capable of synthesizing information from multi-modal resources to address the complex tasks raised from real-life applications. Recently, many large multi-modal datasets have been collected, on which researchers […]


Continue.. SHAPE: An Unified Approach to Evaluate the Contribution and Cooperation of Individual Modalities

Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

Kavli Affiliate: Wei Gao | First 5 Authors: Qian Ma, Wei Gao, Qiang Xiao, Lingsong Ding, Tianyi Gao | Summary: Brain-computer interfaces (BCIs), invasive or non-invasive, have projected unparalleled vision and promise for assisting patients in need to better their interaction with the surroundings. Inspired by the BCI-based rehabilitation technologies for nerve-system impairments and amputation, […]


Continue.. Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels

Kavli Affiliate: Zheng Zhu | First 5 Authors: Kai Wang, Xiangyu Peng, Shuo Yang, Jianfei Yang, Zheng Zhu | Summary: Learning with noisy labels has aroused much research interest since data annotations, especially for large-scale datasets, may be inevitably imperfect. Recent approaches resort to a semi-supervised learning problem by dividing training samples into clean and […]


Continue.. Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels