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Jianqi Ma
Researcher at Fudan University
Publications - 12
Citations - 1460
Jianqi Ma is an academic researcher from Fudan University. The author has contributed to research in topics: Computer science & Minimum bounding box. The author has an hindex of 3, co-authored 6 publications receiving 999 citations.
Papers
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Journal ArticleDOI
Arbitrary-Oriented Scene Text Detection via Rotation Proposals
TL;DR: The Rotation Region Proposal Networks are designed to generate inclined proposals with text orientation angle information that are adapted for bounding box regression to make the proposals more accurately fit into the text region in terms of the orientation.
Journal ArticleDOI
Arbitrary-Oriented Scene Text Detection via Rotation Proposals
TL;DR: RRPN as mentioned in this paper proposes a rotation region proposal network to generate inclined text proposals with text orientation angle information, which is then adapted for bounding box regression to make the proposals more accurately fit into the text region in terms of the orientation.
Proceedings ArticleDOI
Face Recognition via Active Annotation and Learning
TL;DR: An active annotation and learning framework for the face recognition task is introduced and a deep neural network is iteratively trained to choose the examples for further manual annotation.
Proceedings ArticleDOI
A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution
TL;DR: A novel transformer-based module, which leverages global attention mechanism, to exert the semantic guidance of text prior to the text reconstruction process, and proposes a text structure consistency loss to refine the visual appearance by imposing structural consistency on the reconstructions of regular and deformed texts.
Posted Content
RRPN++: Guidance Towards More Accurate Scene Text Detection.
TL;DR: This paper proposes RRPN++ to exploit the potential of RRPN-based model by several improvements, and proposes the Anchor-free Pyramid Proposal Networks (APPN) to generate first-stage proposals, which adopts the anchor-free design to reduce proposal number and accelerate the inference speed.