activity
20182022
most citedPuzzleNet: Scene Text Detection by Segment Context Graph Learning

6 citations · 12 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL20222 cited

Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation

Jiquan Li, Junliang Guo, Yongxin Zhu +4

The task of Grammatical Error Correction (GEC) has received remarkable attention with wide applications in Natural Language Processing (NLP) in recent years. While one of the key p…

cs.CV20221 cited

Scene Consistency Representation Learning for Video Scene Segmentation

Haoqian Wu, Keyu Chen, Yanan Luo +5

A long-term video, such as a movie or TV show, is composed of various scenes, each of which represents a series of shots sharing the same semantic story. Spotting the correct scene…

cs.CL2022

Relational Representation Learning in Visually-Rich Documents

Xin Li, Yan Zheng, Yiqing Hu +5

Relational understanding is critical for a number of visually-rich documents (VRDs) understanding tasks. Through multi-modal pre-training, recent studies provide comprehensive cont…

cs.CV20221 cited

The Devil is in the Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-Training

Hao Liu, Xinghua Jiang, Xin Li +3

The self-supervised Masked Image Modeling (MIM) schema, following "mask-and-reconstruct" pipeline of recovering contents from masked image, has recently captured the increasing int…

cs.CV20222 cited

Knowledge Mining with Scene Text for Fine-Grained Recognition

Hao Wang, Junchao Liao, Tianheng Cheng +5

Recently, the semantics of scene text has been proven to be essential in fine-grained image classification. However, the existing methods mainly exploit the literal meaning of scen…

cs.CV20206 cited

PuzzleNet: Scene Text Detection by Segment Context Graph Learning

Hao Liu, Antai Guo, Deqiang Jiang +2

Recently, a series of decomposition-based scene text detection methods has achieved impressive progress by decomposing challenging text regions into pieces and linking them in a bo…