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Computer vision

Detection, segmentation, depth and recognition research, plus the vision backbones that keep displacing the last generation.

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arXiv AI
Sep 1

Making the Discrete Continuous: Synthetic RAW Augmentations for Fine-Grained Evaluation of Person Detection Performance in Low Light

arXiv:2605.22455v2 Announce Type: replace-cross Abstract: Real-world deployment of AI vision models is both fueled and limited by the data available for training and testing. Real datasets are sparse...

By Valeria Pais, Malena Mendilaharzu, Daniele Faccio, Luis Oala, Christoph Clausen, Bruno Sanguinetti
agentscomputer-visionroboticsbenchmarkssafety
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arXiv Computer Vision
Sep 1

The MYOSAIQ Challenge: Myocardial Segmentation with Automated Infarct Quantification

arXiv:2608.29246v1 Announce Type: cross Abstract: Late gadolinium enhancement (LGE) cardiac magnetic resonance (MR) imaging is the modality of choice to assess myocardial infarction (MI) lesions. Now...

By Olivier Bernard, William A. Romero R., Cyprien Bouton, Celia Goujat, Hang Jung Ling, Pierre-Marc Jodoin, Fumin Guo, Calder Sheagren, Graham Wright, Abdul Qayyum, Moona Mazher, Steven A. Niederer, Hairui Wang, Xiaomei Wu, Franz Thaler, Gernot Plank, Martin Urschler, Ricardo M. Rosales, Esther Pueyo, Nicolas Duchateau, Frederic Cervenansky, Patrick Clarysse, Loic Belle, Thomas Bochaton, Nathan Mewton, Magalie Viallon, Pierre Croisille
computer-visionfine-tuningbenchmarks
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arXiv Machine Learning
Sep 1

Towards Continual Test-Time Adaptation of Vision-Language Models in Open-Vocabulary Semantic Segmentation

arXiv:2608.29923v1 Announce Type: cross Abstract: Open-vocabulary semantic segmentation (OVSS) relies on vision-language alignment to recognize arbitrary text-defined categories, yet this alignment i...

By Chandler Timm C. Doloriel, Yunbei Zhang, Sarthak Kumar Maharana, Muhammad Salman Siddiqui, Tor Kristian Stevik, Fadi Al Machot, Kristian Hovde Liland, Habib Ullah
llmsagentscomputer-visionroboticsmultimodalsafety
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arXiv Machine Learning
Sep 1

MWIR-4-Plastic: The Identification of Complex End-of-Life Industrial Plastic using Mid-wave Infrared Hyperspectral Imaging and Machine Learning

The paper presents the first publicly available hyperspectral imaging (HSI) dataset of shredded black plastics from end‑of‑life vehicle waste, covering four industrial polymers across RGB, VNIR, SWIR, and MWIR modalities. It introduces a multi‑modal spectral‑spatial framework that combines foreground isolation, pixel‑wise classification, and object‑level majority voting, leveraging hyperspectral transformers and chemometric band selection to accurately classify complex black plastics. The study benchmarks nine processing methods—including chemometric, machine learning, and deep learning architectures—providing a reproducible, comprehensive benchmark for industrial hyperspectral object analysis.

By Elias Arbash, Andr\'ea de Lima Ribeiro, Filipa Sim\~oes, Ahmed Jamal Afifi, Aldino Rizaldy, Yuleika Madriz, Samuel Thiele, Sandra Lorenz, Margret Fuchs, Pedram Ghamisi, Paul Scheunders, Richard Gloaguen
llmscomputer-visionmultimodalbenchmarks
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arXiv AI
Sep 1

A Composition-Aware Pretraining Framework for Geospatial Foundation Models

The paper introduces a composition‑aware pretraining framework for geospatial foundation models that explicitly encodes fractional land‑cover mixtures as histogram targets for each satellite image cell. By using Earth Mover’s Distance to distill these composition targets into a 36.8 M‑parameter backbone, the authors demonstrate significant improvements on region‑level tasks such as zero‑shot image retrieval and scene classification, while maintaining competitive performance on fine‑grained tasks like segmentation and object detection. The method outperforms larger models (SatMAE and Prithvi‑EO‑2.0) and achieves a 55.6 % relative boost on the ForestNet‑12 dataset, evidencing the benefit of explicit composition modeling.

By Aryan Kashyap Naveen, Abhishek Srinivas, Pranav Moothedath, Shrutilipi Bhattacharjee
computer-visionbenchmarks
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arXiv AI
Sep 1

Variable-Length Audio Fingerprinting

Variable-Length Audio Fingerprinting (VLAFP) is a new deep learning model that converts audio into low-dimensional fingerprints while allowing variable-length input, unlike existing methods that require fixed-length segments. By supporting variable-length audio during both training and testing, VLAFP captures temporal dynamics that were previously neglected. Experiments demonstrate that VLAFP outperforms current state‑of‑the‑art systems in live audio identification and audio retrieval across three real‑world datasets.

By Hongjie Chen, Hanyu Meng, Huimin Zeng, Ryan A. Rossi, Lie Lu, Josh Kimball
computer-vision
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