62 citations · 66 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
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,…
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…
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…
APNet: Urban-level Scene Segmentation of Aerial Images and Point Clouds
Weijie Wei, Martin R. Oswald, Fatemeh Karimi Nejadasl +1
In this paper, we focus on semantic segmentation method for point clouds of urban scenes. Our fundamental concept revolves around the collaborative utilization of diverse scene rep…
Objects do not disappear: Video object detection by single-frame object location anticipation
Xin Liu, Fatemeh Karimi Nejadasl, Jan C. van Gemert +2
Objects in videos are typically characterized by continuous smooth motion. We exploit continuous smooth motion in three ways. 1) Improved accuracy by using object motion as an addi…
No frame left behind: Full Video Action Recognition
Xin Liu, Silvia L. Pintea, Fatemeh Karimi Nejadasl +2
Not all video frames are equally informative for recognizing an action. It is computationally infeasible to train deep networks on all video frames when actions develop over hundre…
KPRNet: Improving projection-based LiDAR semantic segmentation
Deyvid Kochanov, Fatemeh Karimi Nejadasl, Olaf Booij
Semantic segmentation is an important component in the perception systems of autonomous vehicles. In this work, we adopt recent advances in both image and point cloud segmentation…