no code implementations • 18 Oct 2022 • Xin Li, Botian Shi, Yuenan Hou, Xingjiao Wu, Tianlong Ma, Yikang Li, Liang He
To address these problems, we construct the homogeneous structure between the point cloud and images to avoid projective information loss by transforming the camera features into the LiDAR 3D space.
no code implementations • 23 Jul 2022 • Xiangcheng Du, Zhao Zhou, Yingbin Zheng, Xingjiao Wu, Tianlong Ma, Cheng Jin
Scene text erasing seeks to erase text contents from scene images and current state-of-the-art text erasing models are trained on large-scale synthetic data.
no code implementations • 24 Jan 2022 • Xingjiao Wu, Luwei Xiao, Xiangcheng Du, Yingbin Zheng, Xin Li, Tianlong Ma, Liang He
Our framework is an unsupervised document layout analysis framework.
no code implementations • 27 Nov 2021 • Tianlong Ma, Xingjiao Wu, Xin Li, Xiangcheng Du, Zhao Zhou, Liang Xue, Cheng Jin
To measure the proposed image layer modeling method, we propose a manually-labeled non-Manhattan layout fine-grained segmentation dataset named FPD.
no code implementations • Information Sciences 2021 • Xingjiao Wu, Yingbin Zheng, Tianlong Ma, Hao Ye, Liang He
Layout analysis from a document image plays an important role in document content understanding and information extraction systems.
no code implementations • 4 Aug 2021 • Xingjiao Wu, Tianlong Ma, Xin Li, Qin Chen, Liang He
The HITL select key samples by using confidence.
no code implementations • 2 Aug 2021 • Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, Liang He
Humans can provide training data for machine learning applications and directly accomplish tasks that are hard for computers in the pipeline with the help of machine-based approaches.
no code implementations • 4 Nov 2019 • Xiangcheng Du, Tianlong Ma, Yingbin Zheng, Hao Ye, Xingjiao Wu, Liang He
In this paper, we study text recognition framework by considering the long-term temporal dependencies in the encoder stage.
no code implementations • 4 Jul 2019 • Zhichao Fu, Tianlong Ma, Yingbin Zheng, Hao Ye, Jing Yang, Liang He
In this paper, we resort to human visual demands of sharp edges and propose a two-phase edge-aware deep network to improve deep image deblurring.