activity
20182021
most citedMatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extraction

4 citations · 4 across the 1 of their papers we have counts for

collaborators

6 papers

cs.CV20214 cited

MatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extraction

Guozhi Tang, Lele Xie, Lianwen Jin +6

Visual Information Extraction (VIE) task aims to extract key information from multifarious document images (e.g., invoices and purchase receipts). Most previous methods treat the V…

cs.CV2019

Omnidirectional Scene Text Detection with Sequential-free Box Discretization

Yuliang Liu, Sheng Zhang, Lianwen Jin +3

Scene text in the wild is commonly presented with high variant characteristics. Using quadrilateral bounding box to localize the text instance is nearly indispensable for detection…

cs.CV2019

Aggregation Cross-Entropy for Sequence Recognition

Zecheng Xie, Yaoxiong Huang, Yuanzhi Zhu +3

In this paper, we propose a novel method, aggregation cross-entropy (ACE), for sequence recognition from a brand new perspective. The ACE loss function exhibits competitive perform…

cs.CV2019

Tightness-aware Evaluation Protocol for Scene Text Detection

Yuliang Liu, Lianwen Jin, Zecheng Xie +3

Evaluation protocols play key role in the developmental progress of text detection methods. There are strict requirements to ensure that the evaluation methods are fair, objective…

cs.CV2018

DeRPN: Taking a further step toward more general object detection

Lele Xie, Yuliang Liu, Lianwen Jin +1

Most current detection methods have adopted anchor boxes as regression references. However, the detection performance is sensitive to the setting of the anchor boxes. A proper sett…

cs.CV2018

Detecting Heads using Feature Refine Net and Cascaded Multi-Scale Architecture

Dezhi Peng, Zikai Sun, Zirong Chen +3

This paper presents a method that can accurately detect heads especially small heads under the indoor scene. To achieve this, we propose a novel method, Feature Refine Net (FRN), a…