Search Results for author:
Found 22 papers, 10 papers with code
3rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Results
The 3rd Workshop on Maritime Computer Vision (MaCVi) 2025 addresses maritime computer vision for Unmanned Surface Vehicles (USV) and underwater.
Robust Hyperspectral Image Panshapring via Sparse Spatial-Spectral Representation
High-resolution hyperspectral imaging plays a crucial role in various remote sensing applications, yet its acquisition often faces fundamental limitations due to hardware constraints.
HyFusion: Enhanced Reception Field Transformer for Hyperspectral Image Fusion
To address these issues, we propose HyFusion, a novel Dual-Coupled Network (DCN) framework designed to enhance cross-domain feature extraction and enable effective feature map reusing.
Divide and Conquer: Grounding a Bleeding Areas in Gastrointestinal Image with Two-Stage Model
This study highlights the practical benefits of a two-stage strategy for medical image analysis and sets a new standard for GI bleeding detection and segmentation.
PromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency Adaptation
Recent advances in All-in-One (AiO) RGB image restoration have demonstrated the effectiveness of prompt learning in handling multiple degradations within a single model.
GRACE: Graph-Regularized Attentive Convolutional Entanglement with Laplacian Smoothing for Robust DeepFake Video Detection
As DeepFake video manipulation techniques escalate, posing profound threats, the urgent need to develop efficient detection strategies is underscored.
CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image Fusion
This paper introduces a novel knowledge distillation (KD) framework for HR-MSI/LR-HSI fusion to achieve SR of LR-HSI.
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 - 2x 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.
