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20172024
most citedRTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer

76 citations · 400 across the 32 of their papers we have counts for

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Showing 2019Show all

12 papers · 1 filter

cs.CV201916 cited

HAMBox: Delving into Online High-quality Anchors Mining for Detecting Outer Faces

Yang Liu, Xu Tang, Xiang Wu +3

Current face detectors utilize anchors to frame a multi-task learning problem which combines classification and bounding box regression. Effective anchor design and anchor matching…

cs.CV201945 cited

ACFNet: Attentional Class Feature Network for Semantic Segmentation

Fan Zhang, Yanqin Chen, Zhihang Li +5

Recent works have made great progress in semantic segmentation by exploiting richer context, most of which are designed from a spatial perspective. In contrast to previous works, w…

cs.CV2019

EATEN: Entity-aware Attention for Single Shot Visual Text Extraction

He guo, Xiameng Qin, Jiaming Liu +3

Extracting entity from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts. Most of the existing works employ classical d…

cs.CV2019

ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling -- RRC-LSVT

Yipeng Sun, Zihan Ni, Chee-Kheng Chng +9

Robust text reading from street view images provides valuable information for various applications. Performance improvement of existing methods in such a challenging scenario heavi…

cs.CV2019

ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)

Chee-Kheng Chng, Yuliang Liu, Yipeng Sun +11

This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT) that consists of three major challenges: i) scene text detection, ii) scene text recogn…

cs.CV2019

Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning

Yipeng Sun, Jiaming Liu, Wei Liu +3

Most existing text reading benchmarks make it difficult to evaluate the performance of more advanced deep learning models in large vocabularies due to the limited amount of trainin…