33 citations · 123 across the 26 of their papers we have counts for
26 papers
Distilling Object Detectors With Global Knowledge
Sanli Tang, Zhongyu Zhang, Zhanzhan Cheng +4
Knowledge distillation learns a lightweight student model that mimics a cumbersome teacher. Existing methods regard the knowledge as the feature of each instance or their relations…
Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting
Ying Chen, Liang Qiao, Zhanzhan Cheng +3
End-to-end text spotting has attached great attention recently due to its benefits on global optimization and high maintainability for real applications. However, the input scale h…
E2-AEN: End-to-End Incremental Learning with Adaptively Expandable Network
Guimei Cao, Zhanzhan Cheng, Yunlu Xu +4
Expandable networks have demonstrated their advantages in dealing with catastrophic forgetting problem in incremental learning. Considering that different tasks may need different…
TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents
Zhanzhan Cheng, Peng Zhang, Can Li +6
Recently, automatically extracting information from visually rich documents (e.g., tickets and resumes) has become a hot and vital research topic due to its widespread commercial v…
DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding
Liang Qiao, Hui Jiang, Ying Chen +9
This paper presents DavarOCR, an open-source toolbox for OCR and document understanding tasks. DavarOCR currently implements 19 advanced algorithms, covering 9 different task forms…
PMAL: Open Set Recognition via Robust Prototype Mining
Jing Lu, Yunxu Xu, Hao Li +2
Open Set Recognition (OSR) has been an emerging topic. Besides recognizing predefined classes, the system needs to reject the unknowns. Prototype learning is a potential manner to…
Technical Report for ICCV 2021 Challenge SSLAD-Track3B: Transformers Are Better Continual Learners
Duo Li, Guimei Cao, Yunlu Xu +2
In the SSLAD-Track 3B challenge on continual learning, we propose the method of COntinual Learning with Transformer (COLT). We find that transformers suffer less from catastrophic…
A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning
Linglan Zhao, Dashan Guo, Yunlu Xu +5
Few-shot learning (FSL) aims to learn models that generalize to novel classes with limited training samples. Recent works advance FSL towards a scenario where unlabeled examples ar…
VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations
Peng Zhang, Can Li, Liang Qiao +4
Document layout analysis is crucial for understanding document structures. On this task, vision and semantics of documents, and relations between layout components contribute to th…
Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition
Hui Jiang, Yunlu Xu, Zhanzhan Cheng +5
Text recognition is a popular topic for its broad applications. In this work, we excavate the implicit task, character counting within the traditional text recognition, without add…
LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment
Liang Qiao, Zaisheng Li, Zhanzhan Cheng +6
Table structure recognition is a challenging task due to the various structures and complicated cell spanning relations. Previous methods handled the problem starting from elements…
1st Place Solution to ICDAR 2021 RRC-ICTEXT End-to-end Text Spotting and Aesthetic Assessment on Integrated Circuit
Qiyao Wang, Pengfei Li, Li Zhu +1
This paper presents our proposed methods to ICDAR 2021 Robust Reading Challenge - Integrated Circuit Text Spotting and Aesthetic Assessment (ICDAR RRC-ICTEXT 2021). For the text sp…
MANGO: A Mask Attention Guided One-Stage Scene Text Spotter
Liang Qiao, Ying Chen, Zhanzhan Cheng +4
Recently end-to-end scene text spotting has become a popular research topic due to its advantages of global optimization and high maintainability in real applications. Most methods…
Learning a Domain Classifier Bank for Unsupervised Adaptive Object Detection
Sanli Tang, Zhanzhan Cheng, Shiliang Pu +3
In real applications, object detectors based on deep networks still face challenges of the large domain gap between the labeled training data and unlabeled testing data. To reduce…
Text Recognition in Real Scenarios with a Few Labeled Samples
Jinghuang Lin, Zhanzhan Cheng, Fan Bai +3
Scene text recognition (STR) is still a hot research topic in computer vision field due to its various applications. Existing works mainly focus on learning a general model with a…
Object-QA: Towards High Reliable Object Quality Assessment
Jing Lu, Baorui Zou, Zhanzhan Cheng +4
In object recognition applications, object images usually appear with different quality levels. Practically, it is very important to indicate object image qualities for better appl…
TRIE: End-to-End Text Reading and Information Extraction for Document Understanding
Peng Zhang, Yunlu Xu, Zhanzhan Cheng +5
Since real-world ubiquitous documents (e.g., invoices, tickets, resumes and leaflets) contain rich information, automatic document image understanding has become a hot topic. Most…
SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition
Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng +4
Arbitrary text appearance poses a great challenge in scene text recognition tasks. Existing works mostly handle with the problem in consideration of the shape distortion, including…
Refined Gate: A Simple and Effective Gating Mechanism for Recurrent Units
Zhanzhan Cheng, Yunlu Xu, Mingjian Cheng +4
Recurrent neural network (RNN) has been widely studied in sequence learning tasks, while the mainstream models (e.g., LSTM and GRU) rely on the gating mechanism (in control of how…
Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting
Liang Qiao, Sanli Tang, Zhanzhan Cheng +4
Many approaches have recently been proposed to detect irregular scene text and achieved promising results. However, their localization results may not well satisfy the following te…
Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization
Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng +4
Temporal action localization is an important yet challenging research topic due to its various applications. Since the frame-level or segment-level annotations of untrimmed videos…
REAPS: Towards Better Recognition of Fine-grained Images by Region Attending and Part Sequencing
Peng Zhang, Xinyu Zhu, Zhanzhan Cheng +2
Fine-grained image recognition has been a hot research topic in computer vision due to its various applications. The-state-of-the-art is the part/region-based approaches that first…
You Only Recognize Once: Towards Fast Video Text Spotting
Zhanzhan Cheng, Jing Lu, Yi Niu +3
Video text spotting is still an important research topic due to its various real-applications. Previous approaches usually fall into the four-staged pipeline: text detection in ind…
Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection
Yunlu Xu, Chengwei Zhang, Zhanzhan Cheng +4
This paper proposes a segregated temporal assembly recurrent (STAR) network for weakly-supervised multiple action detection. The model learns from untrimmed videos with only superv…