25 citations · 33 across the 10 of their papers we have counts for
13 papers
Static in Frames, Dynamic in Events: Rethinking Features in Event Cameras as Motion Cues
Hesam Araghi, Jan van Gemert, Nergis Tomen
Event cameras capture intensity changes asynchronously with high temporal resolution, requiring novel preprocessing methods for downstream tasks. Unlike static intensity snapshots,…
Data-Efficient Challenges in Visual Inductive Priors: A Retrospective
Robert-Jan Bruintjes, Attila Lengyel, Osman Semih Kayhan +4
Deep Learning requires large amounts of data to train models that work well. In data-deficient settings, performance can be degraded. We investigate which Deep Learning methods ben…
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling
Hesam Araghi, Jan van Gemert, Nergis Tomen
Event cameras offer high temporal resolution and power efficiency, making them well-suited for edge AI applications. However, their high event rates present challenges for data tra…
Pushing the boundaries of event subsampling in event-based video classification using CNNs
Hesam Araghi, Jan van Gemert, Nergis Tomen
Event cameras offer low-power visual sensing capabilities ideal for edge-device applications. However, their high event rate, driven by high temporal details, can be restrictive in…
VIPriors 4: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
Robert-Jan Bruintjes, Attila Lengyel, Marcos Baptista Rios +4
The fourth edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" workshop features two data-impaired challenges. These challenges address the problem…
Deep Continuous Networks
Nergis Tomen, Silvia L. Pintea, Jan C. van Gemert
CNNs and computational models of biological vision share some fundamental principles, which opened new avenues of research. However, fruitful cross-field research is hampered by co…
Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene Segmentation
Liang Zeng, Attila Lengyel, Nergis Tömen +1
In this work, we leverage estimated depth to boost self-supervised contrastive learning for segmentation of urban scenes, where unlabeled videos are readily available for training…
Heart rate estimation in intense exercise videos
Yeshwanth Napolean, Anwesh Marwade, Nergis Tomen +3
Estimating heart rate from video allows non-contact health monitoring with applications in patient care, human interaction, and sports. Existing work can robustly measure heart rat…
VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
Attila Lengyel, Robert-Jan Bruintjes, Marcos Baptista Rios +4
The second edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges featured five data-impaired challenges, where models are trained from scra…
Frequency learning for structured CNN filters with Gaussian fractional derivatives
Nikhil Saldanha, Silvia L. Pintea, Jan C. van Gemert +1
Frequency information lies at the base of discriminating between textures, and therefore between different objects. Classical CNN architectures limit the frequency learning through…
Resolution learning in deep convolutional networks using scale-space theory
Silvia L. Pintea, Nergis Tomen, Stanley F. Goes +2
Resolution in deep convolutional neural networks (CNNs) is typically bounded by the receptive field size through filter sizes, and subsampling layers or strided convolutions on fea…
Spectral Leakage and Rethinking the Kernel Size in CNNs
Nergis Tomen, Jan van Gemert
Convolutional layers in CNNs implement linear filters which decompose the input into different frequency bands. However, most modern architectures neglect standard principles of fi…
Top-Down Networks: A coarse-to-fine reimagination of CNNs
Ioannis Lelekas, Nergis Tomen, Silvia L. Pintea +1
Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and prefe…