arXiv Machine Learning

Balancing Real and Synthetic Data for CNN-based Masonry Crack Detection

arXiv:2606. 08033v1 Announce Type: cross Abstract: Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages.

arXiv Machine Learning

Jun 18

Investigation of Neural Network Methods for Reconstruction and Classification of Texture Images Under Conditions of Incomplete Information

arXiv:2204. 14224v3 Announce Type: replace-cross Abstract: The automated analysis of heterogeneous natural textures is frequently hindered by physical damage and data loss, presenting a significant challenge to computer vision.

By Galymzhan Abdimanap, Kairat Bostanbekov, Abdelrahman Abdallah, Anel Alimova, Darkhan Kurmangaliyev, Daniyar Nurseitov, Tatyana Dedova, Larissa Balakay, Serik Nurakynov

llms agents diffusion computer-vision benchmarks safety

arXiv AI

Jun 17

Bridging Spatial And Frequency Views For Disaster Assessment: Benefits And Limitations

arXiv:2606. 17403v1 Announce Type: cross Abstract: Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery.

By Shikha V. Chandel, Yadav Raj Ghimire, Timothy Agboada, Leila Hashemi-Beni

safety

arXiv AI

Jun 16

Multi-Modal Attention for Automated Disaster Damage Assessment Using Remote Sensing Imagery and Deep Learning

arXiv:2606. 14963v1 Announce Type: cross Abstract: Timely and accurate disaster damage assessment is crucial for effective emergency response, resource allocation, and recovery.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

multimodal

arXiv AI

Aug 3

Predicting Steel Fatigue Life from Micrographs Using Physics-Informed Deep Learning

arXiv:2607. 28695v1 Announce Type: cross Abstract: Here is the plain text version optimized for arXiv's submission form.

By Aryuemaan Kumar Chowdhury

computer-vision benchmarks

arXiv AI

Jun 16

Clay-CNN Hybrids: Leveraging Geospatial Foundation Models as Auxiliary Context for Landslide Detection

arXiv:2606. 14081v2 Announce Type: replace-cross Abstract: Rapid post-event landslide mapping is essential for disaster response but remains difficult to automate due to extreme class imbalance.

By Huong Binh Vu

computer-vision fine-tuning benchmarks

arXiv AI

Jun 15

Clay-CNN Hybrids: Leveraging Geo-Foundational Models as Auxiliary Context for Landslide Detection

arXiv:2606. 14081v1 Announce Type: cross Abstract: Rapid post-event landslide mapping is essential for disaster response but remains difficult to automate due to extreme class imbalance.

By Huong Binh Vu

computer-vision fine-tuning benchmarks

arXiv AI

Jul 7

Towards Generalizable Deepfake Image Detection with Vision Transformers

arXiv:2604. 17376v2 Announce Type: replace-cross Abstract: In today's day and age, we face a challenge in detecting deepfake images because of the fast evolution of modern generative models and the poor generalization capability of existing methods.

By Kaliki V Srinanda, M Manvith Prabhu, Hemanth K Mogilipalem, Jayavarapu S Abhinai, Vaibhav Santhosh, Aryan Herur, Deepu Vijayasenan

llms fine-tuning benchmarks

arXiv AI

1d ago

Synthetic Data Augmentation for Satellite-Based Analysis of Battle-Damaged Agricultural Fields in Ukraine

arXiv:2608. 16380v1 Announce Type: cross Abstract: Monitoring war-induced damage to agricultural land in Ukraine is important for understanding threats to food security, environmental stability, and post-war recovery.

By Marta Sumyk, Oleksandr Kosovan, Iryna Voitsitska

llms diffusion computer-vision safety

arXiv Machine Learning

Jun 16

Discriminative Span as a Predictor of Synthetic Data Utility via Classifier Reconstruction

arXiv:2605. 09697v3 Announce Type: replace-cross Abstract: In many real-world computer vision applications, including medical imaging and industrial inspection, binary classification tasks are characterized by a severe scarcity of positive samples.

By Radhika Amar Desai, Modigari Narendra

rag computer-vision data-engineering

arXiv Machine Learning

Jun 30

$\mu$Flow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors

arXiv:2606. 30528v1 Announce Type: cross Abstract: Current generative models, including GANs and diffusion models, have reached an outstanding level of photorealism, posing significant risks to privacy and security.

By Orazio Pontorno, Mattia Litrico, Luca Guarnera, Mario Valerio Giuffrida, Sebastiano Battiato

diffusion benchmarks safety

arXiv AI

Jun 15

CottonLeafVision: An Explainable and Robust Deep Learning Framework for Cotton Leaf Disease Classification

arXiv:2606. 14686v1 Announce Type: cross Abstract: Globally, cotton is a highly economically beneficial crop, as the textile industry heavily depends on it.

By Rafi Ahamed, Md. Abir Rahman, Tasnia Tarannum Roza, Munaia Jannat Easha, Md. Asif Khan, Sudeepta Mandal

safety

arXiv Machine Learning

Jul 30

Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models

arXiv:2607. 26580v1 Announce Type: cross Abstract: With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately.

By Nand Lal Yadav, Rajesh Kumar, Satyendra Singh, Sudhakar Singh

Related stories