6 papers
Segment to Focus: Guiding Latent Action Models in the Presence of Distractors
Marcus Fechner, Hamza Adnan, Constantin C. Lüth +3
Latent action models (LAMs) offer a promising path to pre-training embodied agents on large amounts of action-free video. They infer latent actions between consecutive observations…
GoalLadder: Incremental Goal Discovery with Vision-Language Models
Alexey Zakharov, Shimon Whiteson
Natural language can offer a concise and human-interpretable means of specifying reinforcement learning (RL) tasks. The ability to extract rewards from a language instruction can e…
TUN3D: Towards Real-World Scene Understanding from Unposed Images
Anton Konushin, Nikita Drozdov, Bulat Gabdullin +4
Layout estimation and 3D object detection are two fundamental tasks in indoor scene understanding. When combined, they enable the creation of a compact yet semantically rich spatia…
The impact of deep learning aid on the workload and interpretation accuracy of radiologists on chest computed tomography: a cross-over reader study
Anvar Kurmukov, Valeria Chernina, Regina Gareeva +18
Interpretation of chest computed tomography (CT) is time-consuming. Previous studies have measured the time-saving effect of using a deep-learning-based aid (DLA) for CT interpreta…
Technical Design Report of the Spin Physics Detector at NICA
The SPD Collaboration, V. Abazov, V. Abramov +411
The Spin Physics Detector collaboration proposes to install a universal detector in the second interaction point of the NICA collider under construction (JINR, Dubna) to study the…
Hierarchical Loss And Geometric Mask Refinement For Multilabel Ribs Segmentation
Aleksei Leonov, Aleksei Zakharov, Sergey Koshelev +3
Automatic ribs segmentation and numeration can increase computed tomography assessment speed and reduce radiologists mistakes. We introduce a model for multilabel ribs segmentation…