869 citations · 1.3k across the 9 of their papers we have counts for
11 papers
Vision-Based 6-DoF Grasp Pose Estimation for Robot Cloth Unfolding
Domen Tabernik, Peter Nimac, Jan Jerićević +2
Cloth manipulation is a challenging task due to the deformable and high-dimensional nature of cloth, which leads to complex interaction dynamics and perceptual ambiguity arising fr…
Towards a foundation model for astrophysical source detection: An End-to-End Gamma-Ray Data Analysis Pipeline Using Deep Learning
Judit Pérez-Romero, Saptashwa Bhattacharyya, Sascha Caron +9
The increasing volume of gamma-ray data demands new analysis approaches that can handle large-scale datasets while providing robustness for source detection. We present a Deep Lear…
A Dataset and Benchmark for Robotic Cloth Unfolding Grasp Selection: The ICRA 2024 Cloth Competition
Victor-Louis De Gusseme, Thomas Lips, Remko Proesmans +59
Robotic cloth manipulation suffers from a lack of standardized benchmarks and shared datasets for evaluating and comparing different approaches. To address this, we created a bench…
Dense Center-Direction Regression for Object Counting and Localization with Point Supervision
Domen Tabernik, Jon Muhovič, Danijel Skočaj
Object counting and localization problems are commonly addressed with point supervised learning, which allows the use of less labor-intensive point annotations. However, learning b…
Center Direction Network for Grasping Point Localization on Cloths
Domen Tabernik, Jon Muhovič, Matej Urbas +1
Object grasping is a fundamental challenge in robotics and computer vision, critical for advancing robotic manipulation capabilities. Deformable objects, like fabrics and cloths, p…
Mixed supervision for surface-defect detection: from weakly to fully supervised learning
Jakob Božič, Domen Tabernik, Danijel Skočaj
Deep-learning methods have recently started being employed for addressing surface-defect detection problems in industrial quality control. However, with a large amount of data need…
End-to-end training of a two-stage neural network for defect detection
Jakob Božič, Domen Tabernik, Danijel Skočaj
Segmentation-based, two-stage neural network has shown excellent results in the surface defect detection, enabling the network to learn from a relatively small number of samples. I…
Deep Learning for Large-Scale Traffic-Sign Detection and Recognition
Domen Tabernik, Danijel Skočaj
Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides accurate and timely way to manage traffic-sign in…
Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
Domen Tabernik, Samo Šela, Jure Skvarč +1
Automated surface-anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of…
Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks
Domen Tabernik, Matej Kristan, Aleš Leonardis
Convolutional neural networks excel in a number of computer vision tasks. One of their most crucial architectural elements is the effective receptive field size, that has to be man…
Towards Deep Compositional Networks
Domen Tabernik, Matej Kristan, Jeremy L. Wyatt +1
Hierarchical feature learning based on convolutional neural networks (CNN) has recently shown significant potential in various computer vision tasks. While allowing high-quality di…