activity
20202026
most citedKPRNet: Improving projection-based LiDAR semantic segmentation

62 citations · 66 across the 10 of their papers we have counts for

collaborators

10 papers

cs.LG2026

A Semi-Supervised Pipeline for Generalized Behavior Discovery from Animal-Borne Motion Time Series

Fatemeh Karimi Nejadasl, Judy Shamoun-Baranes, Eldar Rakhimberdiev

Learning behavioral taxonomies from animal-borne sensors is challenging because labels are scarce, classes are highly imbalanced, and behaviors may be absent from the annotated set…

cs.CV2025★ 1 cited

A Framework for Multi-View Multiple Object Tracking using Single-View Multi-Object Trackers on Fish Data

Chaim Chai Elchik, Fatemeh Karimi Nejadasl, Seyed Sahand Mohammadi Ziabari +1

Multi-object tracking (MOT) in computer vision has made significant advancements, yet tracking small fish in underwater environments presents unique challenges due to complex 3D mo…

cs.CV2024

MARINE: A Computer Vision Model for Detecting Rare Predator-Prey Interactions in Animal Videos

Zsófia Katona, Seyed Sahand Mohammadi Ziabari, Fatemeh Karimi Nejadasl

Encounters between predator and prey play an essential role in ecosystems, but their rarity makes them difficult to detect in video recordings. Although advances in action recognit…

cs.CV2024★ 1 cited

Leveraging Foundation Models via Knowledge Distillation in Multi-Object Tracking: Distilling DINOv2 Features to FairMOT

Niels G. Faber, Seyed Sahand Mohammadi Ziabari, Fatemeh Karimi Nejadasl

Multiple Object Tracking (MOT) is a computer vision task that has been employed in a variety of sectors. Some common limitations in MOT are varying object appearances, occlusions,…

cs.CV2024

3D-AVS: LiDAR-based 3D Auto-Vocabulary Segmentation

Weijie Wei, Osman Ülger, Fatemeh Karimi Nejadasl +2

Open-Vocabulary Segmentation (OVS) methods offer promising capabilities in detecting unseen object categories, but the category must be known and needs to be provided by a human, e…

cs.CV2023

T-MAE: Temporal Masked Autoencoders for Point Cloud Representation Learning

Weijie Wei, Fatemeh Karimi Nejadasl, Theo Gevers +1

The scarcity of annotated data in LiDAR point cloud understanding hinders effective representation learning. Consequently, scholars have been actively investigating efficacious sel…