1 code implementation • 10 Jul 2022 • Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Bo Du, DaCheng Tao
However, the used point label form implies the reading order of humans, which affects the robustness of Transformer model.
no code implementations • 30 May 2022 • Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, DaCheng Tao
Sequence-to-sequence (seq2seq) learning has become a popular trend for pretraining language models, due to its succinct and universal framework.
1 code implementation • 1 Apr 2022 • Jia Liu, Wenjie Xuan, Yuhang Gan, Juhua Liu, Bo Du
In this paper, we propose an end-to-end Supervised Domain Adaptation framework for cross-domain Change Detection, namely SDACD, to effectively alleviate the domain shift between bi-temporal images for better change predictions.
Change Detection
Change detection for remote sensing images
+1
1 code implementation • 13 Jan 2022 • Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, Hua Jin, DaCheng Tao
To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information.
1 code implementation • AAAI 2022 2021 • Yue He, Chen Chen, Jing Zhang, Juhua Liu, Fengxiang He, Chaoyue Wang, Bo Du
Technically, given the character segmentation maps predicted by a VR model, we construct a subgraph for each instance, where nodes represent the pixels in it and edges are added between nodes based on their spatial similarity.
Ranked #2 on
Scene Text Recognition
on SVTP
(using extra training data)
1 code implementation • 26 Oct 2021 • Juhua Liu, Qihuang Zhong, Liang Ding, Hua Jin, Bo Du, DaCheng Tao
In practice, we formulate the model pretrained on the sampled instances into a knowledge guidance model and a learner model, respectively.
1 code implementation • 3 Aug 2021 • Bo Du, Jian Ye, Jing Zhang, Juhua Liu, DaCheng Tao
Existing methods for arbitrary-shaped text detection in natural scenes face two critical issues, i. e., 1) fracture detections at the gaps in a text instance; and 2) inaccurate detections of arbitrary-shaped text instances with diverse background context.
3 code implementations • 17 May 2020 • Jian Ye, Zhe Chen, Juhua Liu, Bo Du
More specifically, we propose to perceive texts from three levels of feature representations, i. e., character-, word- and global-level, and then introduce a novel text representation fusion technique to help achieve robust arbitrary text detection.
Ranked #1 on
Scene Text Detection
on ICDAR 2015