activity
20182025
most citedIntroducing HOT3D: An Egocentric Dataset for 3D Hand and Object Tracking

2 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025

GoTrack: Generic 6DoF Object Pose Refinement and Tracking

Van Nguyen Nguyen, Christian Forster, Sindi Shkodrani +4

We introduce GoTrack, an efficient and accurate CAD-based method for 6DoF object pose refinement and tracking, which can handle diverse objects without any object-specific training…

cs.CV2024★ 1 cited

HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos

Prithviraj Banerjee, Sindi Shkodrani, Pierre Moulon +11

We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D. The dataset offers over 833 minutes (3.7M+ images) of recordings that feature 19 sub…

cs.CV2024★ 2 cited

Introducing HOT3D: An Egocentric Dataset for 3D Hand and Object Tracking

Prithviraj Banerjee, Sindi Shkodrani, Pierre Moulon +9

We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D. The dataset offers over 833 minutes (more than 3.7M images) of multi-view RGB/monoch…

cs.CV2021

United We Learn Better: Harvesting Learning Improvements From Class Hierarchies Across Tasks

Sindi Shkodrani, Yu Wang, Marco Manfredi +1

Attempts of learning from hierarchical taxonomies in computer vision have been mostly focusing on image classification. Though ways of best harvesting learning improvements from hi…

cs.CV2020

Prior to Segment: Foreground Cues for Weakly Annotated Classes in Partially Supervised Instance Segmentation

David Biertimpel, Sindi Shkodrani, Anil S. Baslamisli +1

Instance segmentation methods require large datasets with expensive and thus limited instance-level mask labels. Partially supervised instance segmentation aims to improve mask pre…

cs.CV2018

Dynamic Adaptation on Non-Stationary Visual Domains

Sindi Shkodrani, Michael Hofmann, Efstratios Gavves

Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of statio…