Computer vision

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

3,537 stories · RSS feed

arXiv AI

Jun 19

The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction

arXiv:2603. 01250v2 Announce Type: replace-cross Abstract: Breast cancer is the most frequently diagnosed malignancy among women worldwide and a leading cause of cancer-related mortality.

By Lidia Garrucho, Smriti Joshi, Kaisar Kushibar, Richard Osuala, Maciej Bobowicz, Xavier Bargall\'o, Paulius Jaru\v{s}evi\v{c}ius, Kai Geissler, Raphael Sch\"afer, Muhammad Alberb, Tony Xu, Anne Martel, Daniel Sleiman, Navchetan Awasthi, Hadeel Awwad, Joan C. Vilanova, Robert Mart\'i, Daan Schouten, Jeong Hoon Lee, Mirabela Rusu, Eleonora Poeta, Luisa Vargas, Eliana Pastor, Maria A. Zuluaga, Jessica K\"achele, Dimitrios Bounias, Alexandra Ertl, Katarzyna Gwo\'zdziewicz, Maria-Laura Cosaka, Pasant M. Abo-Elhoda, Sara W. Tantawy, Shorouq S. Sakrana, Norhan O. Shawky-Abdelfatah, Amr Muhammad Abdo-Salem, Androniki Kozana, Eugen Divjak, Gordana Ivanac, Katerina Nikiforaki, Michail E. Klontzas, Rosa Garc\'ia-Dosd\'a, Meltem Gulsun-Akpinar, O\u{g}uz Lafc{\i}, Carlos Mart\'in-Isla, Oliver D\'iaz, Laura Igual, Karim Lekadir

computer-vision benchmarks safety

arXiv Machine Learning

Jun 19

Adversarial Dependence Minimization

arXiv:2502. 03227v2 Announce Type: replace Abstract: Minimally redundant representations are typically learned by minimizing feature covariance.

By Pierre-Fran\c{c}ois De Plaen, Tinne Tuytelaars, Marc Proesmans, Luc Van Gool

computer-vision safety

arXiv AI

Jun 19

CSWinUNETR: Segmentation of Thin Anatomical Structures in Medical Images

arXiv:2606. 19824v1 Announce Type: cross Abstract: Accurate segmentation of thin, tortuous anatomical structures, such as retinal vessels, cerebral vasculature, and facial wrinkles, remains challenging due to low contrast, frequent discontinuities, and severe class imbalance.

By Junho Moon, Haejun Chung, Ikbeom Jang

llms computer-vision benchmarks

arXiv Machine Learning

Jun 19

Scalable Training of Spatially Grounded 2D Vision-Language Models for Radiology

arXiv:2606. 20477v1 Announce Type: cross Abstract: We study how to train visually grounded vision-language models (VLMs) for radiology without manual spatial annotations.

By Yusuf Salcan (Computer Vision Group, University of Freiburg, Germany, CRIION-AI Lab, Freiburg, Germany), Simon Ging (Computer Vision Group, University of Freiburg, Germany, Adaptive & Agentic AI), Robin Schirrmeister (Department of Radiology, Medical Center -- University of Freiburg, Germany), Philipp Arnold (Department of Radiology, Medical Center -- University of Freiburg, Germany), Elmar Kotter (Department of Radiology, Medical Center -- University of Freiburg, Germany), Behzad Bozorgtabar (Adaptive & Agentic AI), Thomas Brox (Computer Vision Group, University of Freiburg, Germany)

llms computer-vision nlp fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 19

3D-DLP: Self-Supervised 3D Object-Centric Scene Representation Learning

arXiv:2606. 19451v1 Announce Type: new Abstract: We introduce 3D-DLP, a self-supervised object-centric representation learning model that decomposes scene-level RGB-D or voxel observations into a set of 3D latent particles.

By Ellina Zhang, Madhaven Iyengar, Amir Zadeh, Chuan Li, Deepak Pathak, David Held, Tal Daniel

diffusion computer-vision robotics

arXiv AI

Jun 19

AgentFinVQA: A Deployable Multi-Agent Pipeline for Auditable Financial Chart QA

arXiv:2606. 19782v1 Announce Type: new Abstract: Financial chart question answering in regulated settings demands more than accuracy: practitioners must know which answers to trust before acting on them, and many institutions cannot send client data to external model providers.

By Aravind Narayanan, Shaina Raza

llms agents computer-vision nlp

arXiv Machine Learning

Jun 19

A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models

arXiv:2604. 13240v2 Announce Type: replace-cross Abstract: Mapping the spatial distribution of species is essential for conservation policy and invasive species management.

By Augustin de la Brosse, Damien Garreau, Thomas Houet, Thomas Corpetti

llms computer-vision robotics

arXiv AI

Jun 19

HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-trainin

arXiv:2606. 20189v1 Announce Type: cross Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD).

By Maciej Wozniak, Jesper Ericsson, Hariprasath Govindarajan, Truls Nyberg, Thomas Gustafsson, Patric Jensfelt, Olov Andersson

agents diffusion computer-vision robotics efficiency multimodal benchmarks safety

arXiv AI

Jun 19

QueryGaussian: Scalable and Training-Free Open-Vocabulary 3D Instance Retrieval

arXiv:2606. 19733v1 Announce Type: cross Abstract: Efficiently retrieving specific 3D instances from large-scale scenes via natural language prompts remains a formidable challenge in multimedia analysis.

By Xiuyuan Zhu, Ke Lu, Zijie Yang, Chao Yue, Jian Xue, Dongming Zhang

rag computer-vision efficiency benchmarks

arXiv Machine Learning

Jun 19

Integrating national forest inventory, airborne lidar, and satellite imagery for wall-to-wall mapping of forest structure with computer vision

arXiv:2606. 20291v1 Announce Type: new Abstract: Remote sensing is increasingly relied upon to deliver actionable science for forest and wildfire risk management across large landscapes.

By Luke J. Zachmann, David D. Diaz, Vincent A. Landau, Chelsey Walden-Schreiner, Tony Chang, Nathan E. Rutenbeck, Katharyn A. Duffy, Kiarie Ndegwa, Andreas Gros, Scott Conway, Guy Bayes

computer-vision

arXiv AI

Jun 19

Confidence-Aware Automated Assessment of Student-Drawn Scientific Models

arXiv:2606. 20264v1 Announce Type: new Abstract: Student-generated drawings are widely used in science education to assess learners' conceptual understanding in modeling-based tasks aligned with the Next Generation Science Standards (NGSS).

By Luyang Fang, Yingchuan Zhang, Jongchan Park, Zhaoji Wang, Ping Ma, Xiaoming Zhai

llms computer-vision

arXiv AI

Jun 19

Overcoming Labelled Data Scarcity for Defect Classification in Scanning Tunneling Microscopy

arXiv:2506. 01678v2 Announce Type: replace-cross Abstract: Scanning tunnelling microscopy (STM) is a powerful technique for imaging surfaces with atomic resolution, providing insight into physical and chemical processes at the level of single atoms and molecules.

By Nikola L. Kolev, Max Trouton, Filippo Federici Canova, Geoff Thornton, David Z. Gao, Neil J. Curson, Taylor J. Z. Stock

computer-vision

arXiv AI

Jun 19

Speeding up the annotation process in semantic segmentation industrial applications

arXiv:2606. 19934v1 Announce Type: cross Abstract: Current machine learning models commonly require large and well-annotated datasets.

By Marta Fernandez-Moreno, Margarita Guerrero, Rosalia Rementeria, Pablo Mesejo, Raul Moreno

computer-vision benchmarks

arXiv Machine Learning

Jun 19

UNIEGO: Proxies as Mediators for Unified Egocentric Video Representation Learning

arXiv:2606. 20559v1 Announce Type: cross Abstract: Egocentric video understanding is inherently limited by the narrow perspective of wearable cameras: a single viewpoint, a single modality, a single model cannot capture the full richness of human action.

By Wenhao Chi, Arkaprava Sinha, Dominick Reilly, Hieu Le, Srijan Das

computer-vision efficiency benchmarks

arXiv Machine Learning

Jun 19

PU-UNet: Stable Multiplicative Interactions for Medical Image Segmentation

arXiv:2606. 20035v1 Announce Type: cross Abstract: Many dense prediction networks rely on additive feature transformations and model higher-order feature interactions only implicitly.

By Ziyuan Li, Osamah Sufyan, Uwe Jaekel, Babette Dellen

computer-vision efficiency benchmarks

arXiv Machine Learning

Jun 18

Clinically Aligned Geometry Constraints for Robust IVUS Vessel Boundary Segmentation

arXiv:2606. 18723v1 Announce Type: cross Abstract: Intravascular ultrasound (IVUS) lumen and external elastic membrane (EEM) segmentation is important for quantitative coronary plaque burden assessment.

By Yunshu Chen, Litao Yang, Giuseppe Di Giovanni, Jordan Tan, Deval Mehta, Andrew Lin, Derek Chew, Masasi Fujino, Julie Butters, Stephen Nicholls, Zongyuan Ge, Kyung Hoon Cho

computer-vision

arXiv AI

Jun 18

CAOA -- Completion-Assisted Object-CAD Alignment

arXiv:2606. 18429v1 Announce Type: cross Abstract: Accurately aligning CAD models to their corresponding objects in indoor RGB-D scans is a central challenge in 3D semantic reconstruction.

By Hiranya Garbha Kumar, Minhas Kamal, Balakrishnan Prabhakaran

computer-vision benchmarks safety

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 Machine Learning

Jun 18

Quantification of Uncertainty with Adversarial Models in Medical Image Segmentation

arXiv:2606. 18860v1 Announce Type: cross Abstract: Reliable pixel-level uncertainty quantification holds the potential to transform clinical workflows by enabling high-fidelity longitudinal monitoring and distinguishing true pathological changes from artifacts.

By Hana Jebril, Thomas Pinetz, G\"unter Klambauer, Hrvoje Bogunovi\'c

computer-vision safety

arXiv Machine Learning

Jun 18

Global Offshore Wind Infrastructure: Deployment and Operational Dynamics from Dense Sentinel-1 Time Series

arXiv:2604. 20822v2 Announce Type: replace-cross Abstract: The offshore wind energy sector is expanding rapidly, increasing the need for independent, high-temporal-resolution monitoring of infrastructure deployment and operation at global scale.

By Thorsten Hoeser, Felix Bachofer, Claudia Kuenzer

computer-vision benchmarks

Get the daily briefing