Search Results for author:
Found 22 papers, 19 papers with code
The MRI Scanner as a Diagnostic: Image-less Active Sampling
Despite the high diagnostic accuracy of Magnetic Resonance Imaging (MRI), using MRI as a Point-of-Care (POC) disease identification tool poses significant accessibility challenges due to the use of high magnetic field strength and lengthy acquisition times.
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction
Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time.
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement
Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's function and condition in a non-invasive manner.
Context Perception Parallel Decoder for Scene Text Recognition
We first present an empirical study of AR decoding in STR, and discover that the AR decoder not only models linguistic context, but also provides guidance on visual context perception.
Ranked #1 on
Scene Text Recognition
on CUTE80
(using extra training data)
LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network
Next, we propose a dual assignment scheme for speed acceleration.
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images
It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI.
PP-StructureV2: A Stronger Document Analysis System
For Table Recognition model, we utilize PP-LCNet, CSP-PAN and SLAHead to optimize the backbone module, feature fusion module and decoding module, respectively, which improved the table structure accuracy by 6\% with comparable inference speed.
Ranked #1 on
Network Pruning
on CIFAR-100
(Inference Time (ms) metric)
PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR System
For text recognizer, the base model is replaced from CRNN to SVTR, and we introduce lightweight text recognition network SVTR LCNet, guided training of CTC by attention, data augmentation strategy TextConAug, better pre-trained model by self-supervised TextRotNet, UDML, and UIM to accelerate the model and improve the effect.
SVTR: Scene Text Recognition with a Single Visual Model
Dominant scene text recognition models commonly contain two building blocks, a visual model for feature extraction and a sequence model for text transcription.
Ranked #16 on
Scene Text Recognition
on ICDAR2013
PP-Matting: High-Accuracy Natural Image Matting
Also, we propose a semantic context branch (SCB) that adopts a semantic segmentation subtask.
Ranked #4 on
Image Matting
on Distinctions-646
PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model
Real-world applications have high demands for semantic segmentation methods.
Ranked #6 on
Real-Time Semantic Segmentation
on Cityscapes test
PP-YOLOE: An evolved version of YOLO
In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment.
Ranked #1 on
2D Object Detection
on
PP-ShiTu: A Practical Lightweight Image Recognition System
In recent years, image recognition applications have developed rapidly.
PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices
We investigate the applicability of the anchor-free strategy on lightweight object detection models.
Ranked #1 on
Object Detection
on MSCOCO
PP-LCNet: A Lightweight CPU Convolutional Neural Network
We propose a lightweight CPU network based on the MKLDNN acceleration strategy, named PP-LCNet, which improves the performance of lightweight models on multiple tasks.
PP-OCRv2: Bag of Tricks for Ultra Lightweight OCR System
Optical Character Recognition (OCR) systems have been widely used in various of application scenarios.
Optical Character Recognition
Optical Character Recognition (OCR)
Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones
Recently, research efforts have been concentrated on revealing how pre-trained model makes a difference in neural network performance.
HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network
Moreover, Hierarchical-Split block is very flexible and efficient, which provides a large space of potential network architectures for different applications.
PP-OCR: A Practical Ultra Lightweight OCR System
Meanwhile, several pre-trained models for the Chinese and English recognition are released, including a text detector (97K images are used), a direction classifier (600K images are used) as well as a text recognizer (17. 9M images are used).
2nd Place Solution in Google AI Open Images Object Detection Track 2019
We present an object detection framework based on PaddlePaddle.
2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019
We present a retrieval based system for landmark retrieval and recognition challenge. There are five parts in retrieval competition system, including feature extraction and matching to get candidates queue; database augmentation and query extension searching; reranking from recognition results and local feature matching.
