no code implementations • 21 Mar 2023 • Weijia Wu, Yuzhong Zhao, Mike Zheng Shou, Hong Zhou, Chunhua Shen
In contrast, synthetic data can be freely available using a generative model (e. g., DALL-E, Stable Diffusion).
1 code implementation • 18 Nov 2022 • Junyi Bian, Xiaodi Huang, Hong Zhou, Shanfeng Zhu
In this paper, we propose GoSum, a novel graph and reinforcement learning based extractive model for long-paper summarization.
Ranked #4 on Text Summarization on Pubmed
no code implementations • 14 Nov 2022 • Yefei He, Zhenyu Lou, Luoming Zhang, Weijia Wu, Bohan Zhuang, Hong Zhou
To solve this, we propose Softmax-aware Binarization, which dynamically adapts to the data distribution and reduces the error caused by binarization.
no code implementations • 18 Jul 2022 • Wejia Wu, Zhuang Li, Jiahong Li, Chunhua Shen, Hong Zhou, Size Li, Zhongyuan Wang, Ping Luo
Our contributions are three-fold: 1) CoText simultaneously address the three tasks (e. g., text detection, tracking, recognition) in a real-time end-to-end trainable framework.
no code implementations • 16 May 2022 • Yefei He, Luoming Zhang, Weijia Wu, Hong Zhou
Binary neural network leverages the $Sign$ function to binarize real values, and its non-derivative property inevitably brings huge gradient errors during backpropagation.
Ranked #1 on Binarization on ImageNet (Top 1 Accuracy metric)
no code implementations • 8 Apr 2022 • Yefei He, Luoming Zhang, Weijia Wu, Hong Zhou
In this paper, we present a simple yet effective data-free quantization method with accurate activation clipping and adaptive batch normalization.
1 code implementation • 20 Mar 2022 • Weijia Wu, Yuanqiang Cai, Chunhua Shen, Debing Zhang, Ying Fu, Hong Zhou, Ping Luo
Recent video text spotting methods usually require the three-staged pipeline, i. e., detecting text in individual images, recognizing localized text, tracking text streams with post-processing to generate final results.
no code implementations • 11 Jan 2022 • Wei Kang, Liang Xu, Hong Zhou
In this paper, we introduce the concept of observability of targeted state variables for systems that may not be fully observable.
no code implementations • 30 Dec 2021 • Zhuang Li, Weijia Wu, Mike Zheng Shou, Jiahong Li, Size Li, Zhongyuan Wang, Hong Zhou
Semantic representation is of great benefit to the video text tracking(VTT) task that requires simultaneously classifying, detecting, and tracking texts in the video.
3 code implementations • 9 Dec 2021 • Weijia Wu, Yuanqiang Cai, Debing Zhang, Sibo Wang, Zhuang Li, Jiahong Li, Yejun Tang, Hong Zhou
Most existing video text spotting benchmarks focus on evaluating a single language and scenario with limited data.
no code implementations • ICCV 2021 • Jinlei Hou, Yingying Zhang, Qiaoyong Zhong, Di Xie, ShiLiang Pu, Hong Zhou
Surprisingly, by varying the granularity of division on feature maps, we are able to modulate the reconstruction capability of the model for both normal and abnormal samples.
1 code implementation • 26 Nov 2020 • Weijia Wu, Enze Xie, Ruimao Zhang, Wenhai Wang, Hong Zhou, Ping Luo
For example, without using polygon annotations, PSENet achieves an 80. 5% F-score on TotalText  (vs. 80. 9% of fully supervised counterpart), 31. 1% better than training directly with upright bounding box annotations, and saves 80%+ labeling costs.
no code implementations • 29 Oct 2020 • Lap Chi Lau, Hong Zhou
We present a local search framework to design and analyze both combinatorial algorithms and rounding algorithms for experimental design problems.
no code implementations • 22 Apr 2019 • Weijia Wu, Jici Xing, Hong Zhou
In this paper, we propose a pixel-wise method named TextCohesion for scene text detection, which splits a text instance into five key components: a Text Skeleton and four Directional Pixel Regions.
Ranked #1 on Curved Text Detection on SCUT-CTW1500
1 code implementation • 25 Dec 2017 • Jiachi Zhang, Xiaolei Shen, Tianqi Zhuo, Hong Zhou
Since the proposal of fully convolutional neural network (FCNN), it has been widely used in semantic segmentation because of its high accuracy of pixel-wise classification as well as high precision of localization.
no code implementations • 13 Nov 2017 • Xiaolei Shen, Jiachi Zhang, Chenjun Yan, Hong Zhou
The core of our method is to extract features of images based on convolutional neural network and achieve classification by classifier.