no code implementations • 25 Mar 2024 • Xinrui Wang, Yan Jin
This study explores a learning-based tri-finger robotic arm manipulating task, which requires complex movements and coordination among the fingers.
no code implementations • 24 Aug 2023 • Yue Wang, Xinrui Wang, Juntao Li, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Min Zhang
Instruction tuning is instrumental in enabling Large Language Models~(LLMs) to follow user instructions to complete various open-domain tasks.
no code implementations • 7 May 2023 • Wenhai Wan, Xinrui Wang, Ming-Kun Xie, Shao-Yuan Li, Sheng-Jun Huang, Songcan Chen
Learning from noisy data has attracted much attention, where most methods focus on closed-set label noise.
no code implementations • 19 Mar 2023 • Yufeng Yin, Minh Tran, Di Chang, Xinrui Wang, Mohammad Soleymani
Facial action unit detection has emerged as an important task within facial expression analysis, aimed at detecting specific pre-defined, objective facial expressions, such as lip tightening and cheek raising.
no code implementations • 28 Feb 2023 • Xiang Li, Xinrui Wang, Songcan Chen
In Multi-Label Learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge.
no code implementations • 28 Nov 2022 • Xinrui Wang, Zhuoru Li, Xiao Zhou, Yusuke Iwasawa, Yutaka Matsuo
Previous learning based stylization methods suffer from the geometric and semantic gaps between portrait domain and style domain, which obstacles the style information to be correctly transferred to the portrait images, leading to poor stylization quality.
no code implementations • 14 Nov 2022 • Feihu Huang, Xinrui Wang, Junyi Li, Songcan Chen
To fill this gap, in the paper, we study a class of nonconvex minimax optimization, and propose an efficient adaptive federated minimax optimization algorithm (i. e., AdaFGDA) to solve these distributed minimax problems.
no code implementations • 29 Sep 2021 • Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama
Specifically, we disentangle the effects of Adaptive Learning Rate and Momentum of the Adam dynamics on saddle-point escaping and flat minima selection.
no code implementations • CVPR 2021 • Lvmin Zhang, Xinrui Wang, Qingnan Fan, Yi Ji, Chunping Liu
To this end, we create a large-scale dataset with these three components annotated by artists in a human-in-the-loop manner.
1 code implementation • 29 Jun 2020 • Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama
Specifically, we disentangle the effects of Adaptive Learning Rate and Momentum of the Adam dynamics on saddle-point escaping and minima selection.
2 code implementations • CVPR 2020 • Xinrui Wang, Jinze Yu
This paper presents an approach for image cartoonization.