Search Results for author:
Found 34 papers, 14 papers with code
MOSE: Boosting Vision-based Roadside 3D Object Detection with Scene Cues
3D object detection based on roadside cameras is an additional way for autonomous driving to alleviate the challenges of occlusion and short perception range from vehicle cameras.
Retinex-guided Channel-grouping based Patch Swap for Arbitrary Style Transfer
The basic principle of the patch-matching based style transfer is to substitute the patches of the content image feature maps by the closest patches from the style image feature maps.
RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation
Catastrophic forgetting of previous knowledge is a critical issue in continual learning typically handled through various regularization strategies.
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.
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.
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.
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.
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.
Knowledge Distillation
Optical Character Recognition (OCR)
+1
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.
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.
GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition
GMSS has the ability to learn more general representations by integrating multiple self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and contrastive learning tasks.
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).
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.
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
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 #7 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.
1st Place Solution to ICDAR 2021 RRC-ICTEXT End-to-end Text Spotting and Aesthetic Assessment on Integrated Circuit
For the text spotting task, we detect the characters on integrated circuit and classify them based on yolov5 detection model.
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
2D+3D Facial Expression Recognition via Discriminative Dynamic Range Enhancement and Multi-Scale Learning
In 2D+3D facial expression recognition (FER), existing methods generate multi-view geometry maps to enhance the depth feature representation.
3D Facial Expression Recognition
Facial Expression Recognition
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
Optical Character Recognition
Optical Character Recognition (OCR)
+1
