no code implementations • 13 Mar 2024 • Yuxin Tian, Mouxing Yang, Yunfan Li, Dayiheng Liu, Xingzhang Ren, Xi Peng, Jiancheng Lv
A natural expectation for PEFTs is that the performance of various PEFTs is positively related to the data size and fine-tunable parameter size.
no code implementations • 4 Sep 2023 • Yuhao Zhou, Minjia Shi, Yuxin Tian, Yuanxi Li, Qing Ye, Jiancheng Lv
However, a significant challenge arises when coordinating FL with crowd intelligence which diverse client groups possess disparate objectives due to data heterogeneity or distinct tasks.
1 code implementation • 23 Apr 2023 • Dongjingdin Liu, Pengpeng Chen, Miao Yao, Yijing Lu, Zijie Cai, Yuxin Tian
Next, we construct a graph convolution training acceleration mechanism to optimize the back-propagation computing of dynamic graph learning with 55. 08\% speed-up.
Ranked #3 on Skeleton Based Action Recognition on NTU RGB+D 120
no code implementations • 21 Feb 2023 • Yunzhong He, Yuxin Tian, Mengjiao Wang, Feier Chen, Licheng Yu, Maolong Tang, Congcong Chen, Ning Zhang, Bin Kuang, Arul Prakash
In this paper we presents Que2Engage, a search EBR system built towards bridging the gap between retrieval and ranking for end-to-end optimizations.
no code implementations • 6 Apr 2022 • Yuhao Zhou, Minjia Shi, Yuxin Tian, Qing Ye, Jiancheng Lv
Federated learning (FL) is identified as a crucial enabler for large-scale distributed machine learning (ML) without the need for local raw dataset sharing, substantially reducing privacy concerns and alleviating the isolated data problem.
no code implementations • 8 Mar 2022 • Yuxin Tian, Shawn Newsam, Kofi Boakye
Effective image retrieval with text feedback stands to impact a range of real-world applications, such as e-commerce.
no code implementations • 7 Nov 2021 • Yuxin Tian, Yujie Wang, Ming Ouyang, Xuesong Shi
This paper presents a hierarchical segment-based optimization method for Simultaneous Localization and Mapping (SLAM) system.
no code implementations • ICCV 2021 • Zike Yan, Yuxin Tian, Xuesong Shi, Ping Guo, Peng Wang, Hongbin Zha
We introduce an experience replay approach to tackle an exemplary task of continual neural mapping: approximating a continuous signed distance function (SDF) from sequential depth images as a scene geometry representation.
no code implementations • 24 Jun 2021 • Xueqing Deng, Yi Zhu, Yuxin Tian, Shawn Newsam
Neural network-based semantic segmentation has achieved remarkable results when large amounts of annotated data are available, that is, in the supervised case.
no code implementations • 18 Mar 2021 • Yuxin Tian, Jinyu Miao, Xingming Wu, Haosong Yue, Zhong Liu, Weihai Chen
In this paper, we address the challenges of place recognition due to dynamics and confusable patterns by proposing a discriminative and semantic feature selection network, dubbed as DSFeat.
no code implementations • 5 Feb 2021 • Ming Ouyang, Xuesong Shi, Yujie Wang, Yuxin Tian, Yingzhe Shen, Dawei Wang, Peng Wang, Zhiqiang Cao
We present a collaborative visual simultaneous localization and mapping (SLAM) framework for service robots.
1 code implementation • 8 Dec 2020 • Xueqing Deng, Yi Zhu, Yuxin Tian, Shawn Newsam
Land-cover classification using remote sensing imagery is an important Earth observation task.
1 code implementation • 1 Dec 2020 • Dongjiang Li, Jinyu Miao, Xuesong Shi, Yuxin Tian, Qiwei Long, Tianyu Cai, Ping Guo, Hongfei Yu, Wei Yang, Haosong Yue, Qi Wei, Fei Qiao
Experimental results show that the proposed RaP-Net trained with OpenLORIS-Location dataset achieves excellent performance in the feature matching task and significantly outperforms state-of-the-arts feature algorithms in indoor localization.
1 code implementation • 23 Jun 2020 • Mingyuan Mao, Yuxin Tian, Baochang Zhang, Qixiang Ye, Wanquan Liu, Guodong Guo, David Doermann
In this paper, we propose a new feature optimization approach to enhance features and suppress background noise in both the training and inference stages.
no code implementations • 23 Dec 2019 • Xueqing Deng, Yi Zhu, Yuxin Tian, Shawn Newsam
Inspired by this, we investigate methods to inform or guide deep learning models for geospatial image analysis to increase their performance when a limited amount of training data is available or when they are applied to scenarios other than which they were trained on.
no code implementations • 13 Nov 2019 • Xuesong Shi, Dongjiang Li, Pengpeng Zhao, Qinbin Tian, Yuxin Tian, Qiwei Long, Chunhao Zhu, Jingwei Song, Fei Qiao, Le Song, Yangquan Guo, Zhigang Wang, Yimin Zhang, Baoxing Qin, Wei Yang, Fangshi Wang, Rosa H. M. Chan, Qi She
We also design benchmarking metrics for lifelong SLAM, with which the robustness and accuracy of pose estimation are evaluated separately.
1 code implementation • COLING 2018 • Qian Liu, He-Yan Huang, Yang Gao, Xiaochi Wei, Yuxin Tian, Luyang Liu
In this paper, we propose a task-oriented word embedding method and apply it to the text classification task.
Ranked #21 on Text Classification on AG News