Search Results for author:
Found 34 papers, 15 papers with code
Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER
To deal with this problem, this work proposes a cross-document semantic enhancement method, which consists of two modules: 1) To prevent distractions from irrelevant regions in the current document, we design a learnable attention mask mechanism, which is used to adaptively filter redundant information in the current document.
Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning
Adversarial robustness has attracted much attention recently, and the mainstream solution is adversarial training.
HyperMatch: Noise-Tolerant Semi-Supervised Learning via Relaxed Contrastive Constraint
Recent developments of the application of Contrastive Learning in Semi-Supervised Learning (SSL) have demonstrated significant advancements, as a result of its exceptional ability to learn class-aware cluster representations and the full exploitation of massive unlabeled data.
Few-Shot Class-Incremental Learning via Class-Aware Bilateral Distillation
While knowledge distillation, a prevailing technique in CIL, can alleviate the catastrophic forgetting of older classes by regularizing outputs between current and previous model, it fails to consider the overfitting risk of novel classes in FSCIL.
class-incremental learning
Few-Shot Class-Incremental Learning
+4
Distilling Object Detectors With Global Knowledge
Then, a robust distilling module (RDM) is applied to construct the global knowledge based on the prototypes and filtrate noisy global and local knowledge by measuring the discrepancy of the representations in two feature spaces.
Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting
In this paper, to address this problem, we propose a novel cost-efficient Dynamic Low-resolution Distillation (DLD) text spotting framework, which aims to infer images in different small but recognizable resolutions and achieve a better balance between accuracy and efficiency.
DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding
Compared with the previous opensource OCR toolbox, DavarOCR has relatively more complete support for the sub-tasks of the cutting-edge technology of document understanding.
TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents
Most existing methods divide this task into two subparts: the text reading part for obtaining the plain text from the original document images and the information extraction part for extracting key contents.
E2-AEN: End-to-End Incremental Learning with Adaptively Expandable Network
In this paper, we propose an end-to-end trainable adaptively expandable network named E2-AEN, which dynamically generates lightweight structures for new tasks without any accuracy drop in previous tasks.
MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective
NER model has achieved promising performance on standard NER benchmarks.
Ranked #10 on
Named Entity Recognition (NER)
on WNUT 2017
PMAL: Open Set Recognition via Robust Prototype Mining
Accordingly, the embedding space can be better optimized to discriminate therein the predefined classes and between known and unknowns.
Technical Report for ICCV 2021 Challenge SSLAD-Track3B: Transformers Are Better Continual Learners
In the SSLAD-Track 3B challenge on continual learning, we propose the method of COntinual Learning with Transformer (COLT).
STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data
However, in many real-world applications, it is desirable to have SSL algorithms that not only classify the samples drawn from the same distribution of labeled data but also detect out-of-distribution (OOD) samples drawn from an unknown distribution.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning
Few-shot learning (FSL) aims to learn models that generalize to novel classes with limited training samples.
ICDAR 2021 Competition on Scene Video Text Spotting
During the competition period (opened on 1st March, 2021 and closed on 11th April, 2021), a total of 24 teams participated in the three proposed tasks with 46 valid submissions, respectively.
LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment
In this paper, we aim to obtain more reliable aligned bounding boxes by fully utilizing the visual information from both text regions in proposed local features and cell relations in global features.
Ranked #8 on
Table Recognition
on PubTabNet
Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition
In this work, we excavate the implicit task, character counting within the traditional text recognition, without additional labor annotation cost.
VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations
To address the above limitations, we propose a unified framework VSR for document layout analysis, combining vision, semantics and relations.
Ranked #3 on
Document Layout Analysis
on PubLayNet val
Rethinking Pseudo-labeled Sample Mining for Semi-Supervised Object Detection
However, the impact of the pseudo-labeled samples' quality as well as the mining strategies for high quality training sample have rarely been studied in SSL.
MANGO: A Mask Attention Guided One-Stage Scene Text Spotter
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.
Ranked #6 on
Text Spotting
on SCUT-CTW1500
Learning a Domain Classifier Bank for Unsupervised Adaptive Object Detection
To tackle this issue, we develop a fine-grained domain alignment approach with a well-designed domain classifier bank that achieves the instance-level alignment respecting to their categories.
Text Recognition in Real Scenarios with a Few Labeled Samples
Scene text recognition (STR) is still a hot research topic in computer vision field due to its various applications.
Object-QA: Towards High Reliable Object Quality Assessment
In this paper, we define the problem of object quality assessment for the first time and propose an effective approach named Object-QA to assess high-reliable quality scores for object images.
TRIE: End-to-End Text Reading and Information Extraction for Document Understanding
Since real-world ubiquitous documents (e. g., invoices, tickets, resumes and leaflets) contain rich information, automatic document image understanding has become a hot topic.
SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition
Arbitrary text appearance poses a great challenge in scene text recognition tasks.
Refined Gate: A Simple and Effective Gating Mechanism for Recurrent Units
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 information flows between hidden states).
Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting
Many approaches have recently been proposed to detect irregular scene text and achieved promising results.
Ranked #8 on
Text Spotting
on SCUT-CTW1500
Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization
The second module is a specific classifier for mining trivial or incomplete action regions, which is trained on the shared features after erasing the seeded regions activated by SSG.
Action Detection
Weakly-supervised Temporal Action Localization
+1
REAPS: Towards Better Recognition of Fine-grained Images by Region Attending and Part Sequencing
Fine-grained image recognition has been a hot research topic in computer vision due to its various applications.
You Only Recognize Once: Towards Fast Video Text Spotting
Video text spotting is still an important research topic due to its various real-applications.
Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection
Finally, we transform the output of recurrent neural network into the corresponding action distribution.
Edit Probability for Scene Text Recognition
The advantage lies in that the training process can focus on the missing, superfluous and unrecognized characters, and thus the impact of the misalignment problem can be alleviated or even overcome.
AON: Towards Arbitrarily-Oriented Text Recognition
Existing methods on text recognition mainly work with regular (horizontal and frontal) texts and cannot be trivially generalized to handle irregular texts.
Ranked #9 on
Scene Text Recognition
on ICDAR 2003
Focusing Attention: Towards Accurate Text Recognition in Natural Images
FAN consists of two major components: an attention network (AN) that is responsible for recognizing character targets as in the existing methods, and a focusing network (FN) that is responsible for adjusting attention by evaluating whether AN pays attention properly on the target areas in the images.
