Search Results for author: Xin Tang

Found 14 papers, 3 papers with code

Visual-Semantic Transformer for Scene Text Recognition

no code implementations2 Dec 2021 Xin Tang, Yongquan Lai, Ying Liu, Yuanyuan Fu, Rui Fang

In this work, we propose to model semantic and visual information jointly with a Visual-Semantic Transformer (VST).

Irregular Text Recognition Scene Text Recognition

CenterAtt: Fast 2-stage Center Attention Network

no code implementations19 Jun 2021 Jianyun Xu, Xin Tang, Jian Dou, Xu Shu, Yushi Zhu

In this technical report, we introduce the methods of HIKVISION_LiDAR_Det in the challenge of waymo open dataset real-time 3D detection.

SGMNet: Learning Rotation-Invariant Point Cloud Representations via Sorted Gram Matrix

no code implementations ICCV 2021 Jianyun Xu, Xin Tang, Yushi Zhu, Jie Sun, ShiLiang Pu

Recently, various works that attempted to introduce rotation invariance to point cloud analysis have devised point-pair features, such as angles and distances.

A Two-Stage Approach to Device-Robust Acoustic Scene Classification

1 code implementation3 Nov 2020 Hu Hu, Chao-Han Huck Yang, Xianjun Xia, Xue Bai, Xin Tang, Yajian Wang, Shutong Niu, Li Chai, Juanjuan Li, Hongning Zhu, Feng Bao, Yuanjun Zhao, Sabato Marco Siniscalchi, Yannan Wang, Jun Du, Chin-Hui Lee

To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed.

Acoustic Scene Classification Classification +3

Learning Graph Normalization for Graph Neural Networks

1 code implementation24 Sep 2020 Yihao Chen, Xin Tang, Xianbiao Qi, Chun-Guang Li, Rong Xiao

We conduct extensive experiments on benchmark datasets for different tasks, including node classification, link prediction, graph classification and graph regression, and confirm that the learned graph normalization leads to competitive results and that the learned weights suggest the appropriate normalization techniques for the specific task.

Graph Classification Graph Regression +2

Joint Multi-User DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence

no code implementations15 Jul 2020 Xin Tang, Xu Chen, Liekang Zeng, Shuai Yu, Lin Chen

With the assistance of edge servers, user equipments (UEs) are able to run deep neural network (DNN) based AI applications, which are generally resource-hungry and compute-intensive, such that an individual UE can hardly afford by itself in real time.

Edge-computing

Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages

no code implementations20 Oct 2018 Xin Tang, Shanbo Cheng, Loc Do, Zhiyu Min, Feng Ji, Heng Yu, Ji Zhang, Haiqin Chen

Our approach is extended from a basic monolingual STS framework to a shared multilingual encoder pretrained with translation task to incorporate rich-resource language data.

Machine Translation Semantic Similarity +2

RGB Video Based Tennis Action Recognition Using a Deep Historical Long Short-Term Memory

no code implementations2 Aug 2018 Jia-xin Cai, Xin Tang

Action recognition has attracted increasing attention from RGB input in computer vision partially due to potential applications on somatic simulation and statistics of sport such as virtual tennis game and tennis techniques and tactics analysis by video.

Action Recognition Frame

Learning zeroth class dictionary for human action recognition

no code implementations13 Mar 2016 Jia-xin Cai, Xin Tang, Lifang Zhang, Guocan Feng

In this paper, a discriminative two-phase dictionary learning framework is proposed for classifying human action by sparse shape representations, in which the first-phase dictionary is learned on the selected discriminative frames and the second-phase dictionary is built for recognition using reconstruction errors of the first-phase dictionary as input features.

Action Recognition Dictionary Learning

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