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Found 15 papers, 5 papers with code
Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat
This paper addresses the challenges associated with hyperspectral image (HSI) reconstruction from miniaturized satellites, which often suffer from stripe effects and are computationally resource-limited.
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
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.
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).
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.
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.
