1 code implementation • 1 May 2024 • Zhiyu Fang, Jingyan Qin, Xiaobin Zhu, Chun Yang, Xu-Cheng Yin
Distinguished from traditional knowledge graphs (KGs), temporal knowledge graphs (TKGs) must explore and reason over temporally evolving facts adequately.
1 code implementation • 1 May 2024 • Zhiyu Fang, Shuai-Long Lei, Xiaobin Zhu, Chun Yang, Shi-Xue Zhang, Xu-Cheng Yin, Jingyan Qin
We then craft a mixed-context reasoning module based on the multi-layer perceptron (MLP) to learn the unified representations of inter-quadruples for ECE while accomplishing temporal knowledge reasoning.
no code implementations • 8 Jan 2024 • Shi-Xue Zhang, Chun Yang, Xiaobin Zhu, Hongyang Zhou, Hongfa Wang, Xu-Cheng Yin
Specifically, we propose an innovative reading-order estimation module (REM) that extracts reading-order information from the initial text boundary generated by an initial boundary module (IBM).
1 code implementation • 13 Jul 2023 • Zhan Shi, Xin Ding, Peng Ding, Chun Yang, Ru Huang, Xiaoxuan Song
Four tiny SOAP models are also created by replacing the convolutional blocks in Mobile-SOAP with four small-scale networks, respectively.
no code implementations • 5 Sep 2022 • Lei Chen, Haibo Qin, Shi-Xue Zhang, Chun Yang, XuCheng Yin
In this paper, we propose an efficient attention-free Single-Point Decoding Network (dubbed SPDN) for scene text recognition, which can replace the traditional attention-based decoding network.
no code implementations • 7 Jul 2022 • Chun Yang, Shicai Fan
Currently, two popular loss functions are widely used to optimize recommender systems: the pointwise and the pairwise.
2 code implementations • 11 May 2022 • Shi-Xue Zhang, Chun Yang, Xiaobin Zhu, Xu-Cheng Yin
In our method, we explicitly model the text boundary via an innovative iterative boundary transformer in a coarse-to-fine manner.
1 code implementation • CVPR 2022 • Chang Liu, Chun Yang, Xu-Cheng Yin
Contextual information can be decomposed into temporal information and linguistic information.
no code implementations • 22 Mar 2022 • Shixiao Fan, Xuan Cheng, Xiaomin Wang, Chun Yang, Pan Deng, Minghui Liu, Jiali Deng, Ming Liu
Recently, researchers have shown an increased interest in the online knowledge distillation.
1 code implementation • 12 Mar 2022 • Shi-Xue Zhang, Xiaobin Zhu, Jie-Bo Hou, Chun Yang, Xu-Cheng Yin
In this paper, we propose an innovative Kernel Proposal Network (dubbed KPN) for arbitrary shape text detection.
no code implementations • 10 Mar 2022 • Chang Liu, Chun Yang, Hai-Bo Qin, Xiaobin Zhu, Cheng-Lin Liu, Xu-Cheng Yin
Scene text recognition is a popular topic and extensively used in the industry.
no code implementations • 10 Jan 2022 • Chun Yang
In this work, we aim to consider the application of contrastive learning in the scenario of the recommendation system adequately, making it more suitable for recommendation task.
no code implementations • 24 Dec 2021 • Zhiyu Fang, Xiaobin Zhu, Chun Yang, Zheng Han, Jingyan Qin, Xu-Cheng Yin
Learning a common latent embedding by aligning the latent spaces of cross-modal autoencoders is an effective strategy for Generalized Zero-Shot Classification (GZSC).
no code implementations • 3 Dec 2021 • Chun Yang
Besides, BERT model applies absolute position embedding to introduce contextual information to the model, which would bring noise to the raw data and therefore cannot be applied to fault diagnosis directly.
1 code implementation • ICCV 2021 • Shi-Xue Zhang, Xiaobin Zhu, Chun Yang, Hongfa Wang, Xu-Cheng Yin
In this work, we propose a novel adaptive boundary proposal network for arbitrary shape text detection, which can learn to directly produce accurate boundary for arbitrary shape text without any post-processing.
no code implementations • 14 Apr 2021 • Chun Yang, Franz Rottensteiner, Christian Heipke
In this paper, a hierarchical deep learning framework is proposed to verify the land use information.
1 code implementation • 27 Oct 2020 • Song-Lu Chen, Shu Tian, Jia-Wei Ma, Qi Liu, Chun Yang, Feng Chen, Xu-Cheng Yin
Second, we propose to predict the quadrilateral bounding box in the local region by regressing the four corners of the license plate to robustly detect oblique license plates.
1 code implementation • 24 Oct 2020 • Zan-Xia Jin, Heran Wu, Chun Yang, Fang Zhou, Jingyan Qin, Lei Xiao, Xu-Cheng Yin
Text-based visual question answering (VQA) requires to read and understand text in an image to correctly answer a given question.
Optical Character Recognition Optical Character Recognition (OCR) +2
no code implementations • 10 Aug 2020 • Po-Heng Chen, Zhao-Xu Luo, Zu-Kuan Huang, Chun Yang, Kuan-Wen Chen
To show the practicality, we further evaluate IF-Net on the task of visual localization under large illumination changes scenes, and achieves the best localization accuracy.
Ranked #1 on Image Stitching on HPatches
2 code implementations • CVPR 2020 • Shi-Xue Zhang, Xiaobin Zhu, Jie-Bo Hou, Chang Liu, Chun Yang, Hongfa Wang, Xu-Cheng Yin
In this paper, we propose a novel unified relational reasoning graph network for arbitrary shape text detection.
no code implementations • 3 Apr 2019 • Xinjie Li, Chun Yang, Songlu Chen, Chao Zhu, Xu-Cheng Yin
Specifically, we design a generalized cross-entropy loss for the training of the proposed framework to fully exploit the semantic priors via considering the relevance between adjacent levels and enlarge the distance between samples of different coarse classes.
no code implementations • 26 Oct 2018 • Michael Ying Yang, Wentong Liao, Chun Yang, Yanpeng Cao, Bodo Rosenhahn
The experimental results show that the proposed approach outperforms the state-of-the-art methods and effective in recognizing complex security events.
no code implementations • 10 Oct 2017 • Chun Yang, Xu-Cheng Yin, Zejun Li, Jianwei Wu, Chunchao Guo, Hongfa Wang, Lei Xiao
Recognizing text in the wild is a really challenging task because of complex backgrounds, various illuminations and diverse distortions, even with deep neural networks (convolutional neural networks and recurrent neural networks).
no code implementations • 4 Jun 2014 • Xu-Cheng Yin, Chun Yang, Hong-Wei Hao
In this paper, we argue that diversity, not direct diversity on samples but adaptive diversity with data, is highly correlated to ensemble accuracy, and we propose a novel technology for classifier ensemble, learning to diversify, which learns to adaptively combine classifiers by considering both accuracy and diversity.