1 citations · 1 across the 4 of their papers we have counts for
4 papers
Multimodal Scenario Similarity Search for Autonomous Driving
Tamás Matuszka, András Tamásy, Balázs Szolár
Large-scale autonomous-driving datasets contain vast numbers of recorded scenarios, creating a need for efficient retrieval methods that can identify situations similar to a given…
Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving
Sándor Kunsági-Máté, Levente Pető, Lehel Seres +1
3D detection of traffic management objects, such as traffic lights and road signs, is vital for self-driving cars, particularly for address-to-address navigation where vehicles enc…
Zero-shot 3D General Obstacle Detection via Multimodal Foundation Models and Geometry
Tamás Matuszka, Péter Hajas, Dávid Szeghy
Detecting general obstacles is critical for autonomous driving, especially in long-tail scenarios with rare or unseen objects. Existing methods rely on supervision or predefined ca…
Compute-Efficient Active Learning
Gábor Németh, Tamás Matuszka
Active learning, a powerful paradigm in machine learning, aims at reducing labeling costs by selecting the most informative samples from an unlabeled dataset. However, the traditio…