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Found 22 papers, 8 papers with code
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Progressive Alignment with VLM-LLM Feature to Augment Defect Classification for the ASE Dataset
In this work, we propose the special ASE dataset, including rich data description recorded on image, for defect classification, but the defect feature is uneasy to learn directly.
A Closer Look at Spatial-Slice Features Learning for COVID-19 Detection
Conventional Computed Tomography (CT) imaging recognition faces two significant challenges: (1) There is often considerable variability in the resolution and size of each CT scan, necessitating strict requirements for the input size and adaptability of models.
DRCT: Saving Image Super-resolution away from Information Bottleneck
In recent years, Vision Transformer-based approaches for low-level vision tasks have achieved widespread success.
Ranked #1 on
Image Super-Resolution
on Set14 - 4x upscaling
OCR is All you need: Importing Multi-Modality into Image-based Defect Detection System
To address this, we introduce an external modality-guided data mining framework, primarily rooted in optical character recognition (OCR), to extract statistical features from images as a second modality to enhance performance, termed OANet (Ocr-Aoi-Net).
MISS: Memory-efficient Instance Segmentation Framework By Visual Inductive Priors Flow Propagation
Instance segmentation, a cornerstone task in computer vision, has wide-ranging applications in diverse industries.
Augment Before Copy-Paste: Data and Memory Efficiency-Oriented Instance Segmentation Framework for Sport-scenes
Instance segmentation is a fundamental task in computer vision with broad applications across various industries.
Simple 2D Convolutional Neural Network-based Approach for COVID-19 Detection
This study explores the use of deep learning techniques for analyzing lung Computed Tomography (CT) images.
QuantTune: Optimizing Model Quantization with Adaptive Outlier-Driven Fine Tuning
Firstly, our analysis revealed that, on average, 65\% of quantization errors result from the precision loss incurred by the dynamic range amplification effect of outliers across the target Transformer-based models.
Strong Baseline and Bag of Tricks for COVID-19 Detection of CT Scans
This paper investigates the application of deep learning models for lung Computed Tomography (CT) image analysis.
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection.
Spatiotemporal Feature Learning Based on Two-Step LSTM and Transformer for CT Scans
However, the nature of the CT images is even more diverse since the resolution and number of the slices of a CT scan are determined by the machine and its settings.
ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images
The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions.
Visual Transformer with Statistical Test for COVID-19 Classification
The frame-level feature is extracted from each CT slice based on any backbone network and followed by feeding the features to our within-slice-Transformer (WST) to discover the context information in the pixel dimension.
Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report
As the quality of mobile cameras starts to play a crucial role in modern smartphones, more and more attention is now being paid to ISP algorithms used to improve various perceptual aspects of mobile photos.
Edge-Preserving Guided Semantic Segmentation for VIPriors Challenge
Then, the two-dimensional cross-entropy loss is adopted to calculate the loss between the predicted edge map and its ground truth, termed as an edge-preserving loss.
Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution
Recently, an enhanced super-resolution based on generative adversarial network (ESRGAN) has achieved excellent performance in terms of both qualitative and quantitative quality of the reconstructed high-resolution image.
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results
For training, only one set of source input images is therefore provided in the challenge.
Learning to Detect Fake Face Images in the Wild
Although Generative Adversarial Network (GAN) can be used to generate the realistic image, improper use of these technologies brings hidden concerns.
SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination
Despite generative adversarial networks (GANs) can hallucinate photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they cannot guarantee preserving the identities of hallucinated HR faces, making the HR faces poorly recognizable.
CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data
Given a large unlabeled set of images, how to efficiently and effectively group them into clusters based on extracted visual representations remains a challenging problem.
