1 code implementation • 29 Mar 2024 • Yan Luo, Min Shi, Muhammad Osama Khan, Muhammad Muneeb Afzal, Hao Huang, Shuaihang Yuan, Yu Tian, Luo Song, Ava Kouhana, Tobias Elze, Yi Fang, Mengyu Wang
Fairness is a critical concern in deep learning, especially in healthcare, where these models influence diagnoses and treatment decisions.
no code implementations • 14 Feb 2024 • Congcong Wen, Jiazhao Liang, Shuaihang Yuan, Hao Huang, Yi Fang
In the field of robotics and automation, navigation systems based on Large Language Models (LLMs) have recently shown impressive performance.
no code implementations • 31 Oct 2023 • Yu Hao, Fan Yang, Hao Huang, Shuaihang Yuan, Sundeep Rangan, John-Ross Rizzo, Yao Wang, Yi Fang
By combining the prompt and input image, a large vision-language model (i. e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing the environmental objects and scenes, relevant to the prompt.
no code implementations • 11 Jul 2022 • Jie Qin, Shuaihang Yuan, Jiaxin Chen, Boulbaba Ben Amor, Yi Fang, Nhat Hoang-Xuan, Chi-Bien Chu, Khoi-Nguyen Nguyen-Ngoc, Thien-Tri Cao, Nhat-Khang Ngo, Tuan-Luc Huynh, Hai-Dang Nguyen, Minh-Triet Tran, Haoyang Luo, Jianning Wang, Zheng Zhang, Zihao Xin, Yang Wang, Feng Wang, Ying Tang, Haiqin Chen, Yan Wang, Qunying Zhou, Ji Zhang, Hongyuan Wang
We define two SBSR tasks and construct two benchmarks consisting of more than 46, 000 CAD models, 1, 700 realistic models, and 145, 000 sketches in total.
no code implementations • 8 Oct 2021 • Yu Hao, Hao Huang, Shuaihang Yuan, Yi Fang
We show in experiments that our meta-learning approach, denoted as Meta-3DSeg, leads to improvements on unsupervised 3D shape segmentation over the conventional designs of deep neural networks for 3D shape segmentation functions.
no code implementations • 29 Sep 2021 • Shuaihang Yuan, Yi Fang
In addition, CoLAV introduces a novel mechanism for the dynamic generation of shape-instance-dependent adversarial views as positive pairs to adversarially train robust contrastive learning models towards the learning of more informative 3D shape representation.
no code implementations • 24 Jun 2020 • Shuaihang Yuan, Xiang Li, Anthony Tzes, Yi Fang
To approach this problem, we propose a self-supervised approach that leverages the power of the deep neural network to learn a continuous flow function of 3D point clouds that can predict temporally consistent future motions and naturally bring out the correspondences among consecutive point clouds at the same time.
no code implementations • 24 Jun 2020 • Shuaihang Yuan, Xiang Li, Yi Fang
In this paper, we aim at handling the problem of 3D tracking, which provides the tracking of the consecutive frames of 3D shapes.