22 citations · 53 across the 6 of their papers we have counts for
19 papers
Learning to Discover and Detect Objects
Vladimir Fomenko, Ismail Elezi, Deva Ramanan +2
We tackle the problem of novel class discovery and localization (NCDL). In this setting, we assume a source dataset with supervision for only some object classes. Instances of othe…
Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?
Patrick Dendorfer, Vladimir Yugay, Aljoša Ošep +1
Recent developments in monocular multi-object tracking have been very successful in tracking visible objects and bridging short occlusion gaps, mainly relying on data-driven appear…
Text2Pos: Text-to-Point-Cloud Cross-Modal Localization
Manuel Kolmet, Qunjie Zhou, Aljosa Osep +1
Natural language-based communication with mobile devices and home appliances is becoming increasingly popular and has the potential to become natural for communicating with mobile…
MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?
Matteo Fabbri, Guillem Braso, Gianluca Maugeri +6
Deep learning-based methods for video pedestrian detection and tracking require large volumes of training data to achieve good performance. However, data acquisition in crowded pub…
(Just) A Spoonful of Refinements Helps the Registration Error Go Down
Sérgio Agostinho, Aljoša Ošep, Alessio Del Bue +1
We tackle data-driven 3D point cloud registration. Given point correspondences, the standard Kabsch algorithm provides an optimal rotation estimate. This allows to train registrati…
EagerMOT: 3D Multi-Object Tracking via Sensor Fusion
Aleksandr Kim, Aljoša Ošep, Laura Leal-Taixé
Multi-object tracking (MOT) enables mobile robots to perform well-informed motion planning and navigation by localizing surrounding objects in 3D space and time. Existing methods r…