234 citations · 2k across the 144 of their papers we have counts for
16 papers · 2 filters
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…
Dynamic Instance Normalization for Arbitrary Style Transfer
Yongcheng Jing, Xiao Liu, Yukang Ding +4
Prior normalization methods rely on affine transformations to produce arbitrary image style transfers, of which the parameters are computed in a pre-defined way. Such manually-defi…
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…
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…
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…
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…
Perspective-Guided Convolution Networks for Crowd Counting
Zhaoyi Yan, Yuchen Yuan, Wangmeng Zuo +4
In this paper, we propose a novel perspective-guided convolution (PGC) for convolutional neural network (CNN) based crowd counting (i.e. PGCNet), which aims to overcome the dramati…
Image Inpainting with Learnable Bidirectional Attention Maps
Chaohao Xie, Shaohui Liu, Chao Li +5
Most convolutional network (CNN)-based inpainting methods adopt standard convolution to indistinguishably treat valid pixels and holes, making them limited in handling irregular ho…
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…
An End-to-end Video Text Detector with Online Tracking
Hongyuan Yu, Chengquan Zhang, Xuan Li +3
Video text detection is considered as one of the most difficult tasks in document analysis due to the following two challenges: 1) the difficulties caused by video scenes, i.e., mo…
A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning
Pengfei Wang, Chengquan Zhang, Fei Qi +6
Detecting scene text of arbitrary shapes has been a challenging task over the past years. In this paper, we propose a novel segmentation-based text detector, namely SAST, which emp…
Editing Text in the Wild
Liang Wu, Chengquan Zhang, Jiaming Liu +4
In this paper, we are interested in editing text in natural images, which aims to replace or modify a word in the source image with another one while maintaining its realistic look…
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
Tianwei Lin, Xiao Liu, Xin Li +2
Temporal action proposal generation is an challenging and promising task which aims to locate temporal regions in real-world videos where action or event may occur. Current bottom-…
STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
Ming Liu, Yukang Ding, Min Xia +4
Arbitrary attribute editing generally can be tackled by incorporating encoder-decoder and generative adversarial networks. However, the bottleneck layer in encoder-decoder usually…
Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes
Chengquan Zhang, Borong Liang, Zuming Huang +4
Previous scene text detection methods have progressed substantially over the past years. However, limited by the receptive field of CNNs and the simple representations like rectang…
Detecting Text in the Wild with Deep Character Embedding Network
Jiaming Liu, Chengquan Zhang, Yipeng Sun +2
Most text detection methods hypothesize texts are horizontal or multi-oriented and thus define quadrangles as the basic detection unit. However, text in the wild is usually perspec…