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20172023
most citedFew Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection

11 citations · 31 across the 7 of their papers we have counts for

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8 papers · 1 filter

cs.CV202211 cited

Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection

Jingqun Tang, Wenqing Zhang, Hongye Liu +4

Recently, transformer-based methods have achieved promising progresses in object detection, as they can eliminate the post-processes like NMS and enrich the deep representations. H…

cs.CV20211 cited

DeepAVO: Efficient Pose Refining with Feature Distilling for Deep Visual Odometry

Ran Zhu, Mingkun Yang, Wang Liu +3

The technology for Visual Odometry (VO) that estimates the position and orientation of the moving object through analyzing the image sequences captured by on-board cameras, has bee…

cs.CV20211 cited

Scene Text Retrieval via Joint Text Detection and Similarity Learning

Hao Wang, Xiang Bai, Mingkun Yang +3

Scene text retrieval aims to localize and search all text instances from an image gallery, which are the same or similar to a given query text. Such a task is usually realized by m…

cs.CV2020

AutoSTR: Efficient Backbone Search for Scene Text Recognition

Hui Zhang, Quanming Yao, Mingkun Yang +2

Scene text recognition (STR) is very challenging due to the diversity of text instances and the complexity of scenes. The community has paid increasing attention to boost the perfo…

cs.CV20199 cited

ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard

Xi Liu, Rui Zhang, Yongsheng Zhou +13

Chinese scene text reading is one of the most challenging problems in computer vision and has attracted great interest. Different from English text, Chinese has more than 6000 comm…

cs.CV20195 cited

All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting

Hao Wang, Pu Lu, Hui Zhang +6

Recently, end-to-end text spotting that aims to detect and recognize text from cluttered images simultaneously has received particularly growing interest in computer vision. Differ…