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 • 4 Jul 2022 • Yuzhong Zhao, Yuanqiang Cai, Weijia Wu, Weiqiang Wang
Generally pre-training and long-time training computation are necessary for obtaining a good-performance text detector based on deep networks.
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 • 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 • 10 Sep 2021 • Jue Wang, Haofan Wang, Jincan Deng, Weijia Wu, Debing Zhang
Extra rich non-paired single-modal text data is used for boosting the generalization of text branch.
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 [3] (vs. 80. 9% of fully supervised counterpart), 31. 1% better than training directly with upright bounding box annotations, and saves 80%+ labeling costs.
1 code implementation • 3 Sep 2020 • Weijia Wu, Ning Lu, Enze Xie
To address the severe domain distribution mismatch, we propose a synthetic-to-real domain adaptation method for scene text detection, which transfers knowledge from synthetic data (source domain) to real data (target domain).
no code implementations • 18 Nov 2019 • Qiang Huang, Jianhui Bu, Weijian Xie, Shengwen Yang, Weijia Wu, Li-Ping Liu
Sentence matching is an essential task in the QA systems and is usually reformulated as a Paraphrase Identification (PI) problem.
Ranked #13 on
Paraphrase Identification
on Quora Question Pairs
(Accuracy metric)
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