Picture for Nergis Tomen

Nergis Tomen

Pushing the boundaries of event subsampling in event-based video classification using CNNs

Add code
Sep 13, 2024
Viaarxiv icon

VIPriors 4: Visual Inductive Priors for Data-Efficient Deep Learning Challenges

Add code
Jun 26, 2024
Viaarxiv icon

Deep Continuous Networks

Add code
Feb 02, 2024
Viaarxiv icon

VIPriors 3: Visual Inductive Priors for Data-Efficient Deep Learning Challenges

Add code
May 31, 2023
Viaarxiv icon

Heart rate estimation in intense exercise videos

Add code
Aug 04, 2022
Figure 1 for Heart rate estimation in intense exercise videos
Figure 2 for Heart rate estimation in intense exercise videos
Figure 3 for Heart rate estimation in intense exercise videos
Figure 4 for Heart rate estimation in intense exercise videos
Viaarxiv icon

VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges

Add code
Jan 21, 2022
Figure 1 for VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
Figure 2 for VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
Figure 3 for VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
Figure 4 for VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
Viaarxiv icon

Frequency learning for structured CNN filters with Gaussian fractional derivatives

Add code
Nov 12, 2021
Figure 1 for Frequency learning for structured CNN filters with Gaussian fractional derivatives
Figure 2 for Frequency learning for structured CNN filters with Gaussian fractional derivatives
Figure 3 for Frequency learning for structured CNN filters with Gaussian fractional derivatives
Figure 4 for Frequency learning for structured CNN filters with Gaussian fractional derivatives
Viaarxiv icon

Resolution learning in deep convolutional networks using scale-space theory

Add code
Jun 30, 2021
Figure 1 for Resolution learning in deep convolutional networks using scale-space theory
Figure 2 for Resolution learning in deep convolutional networks using scale-space theory
Figure 3 for Resolution learning in deep convolutional networks using scale-space theory
Figure 4 for Resolution learning in deep convolutional networks using scale-space theory
Viaarxiv icon

Spectral Leakage and Rethinking the Kernel Size in CNNs

Add code
Jan 25, 2021
Figure 1 for Spectral Leakage and Rethinking the Kernel Size in CNNs
Figure 2 for Spectral Leakage and Rethinking the Kernel Size in CNNs
Figure 3 for Spectral Leakage and Rethinking the Kernel Size in CNNs
Figure 4 for Spectral Leakage and Rethinking the Kernel Size in CNNs
Viaarxiv icon

Top-Down Networks: A coarse-to-fine reimagination of CNNs

Add code
Apr 16, 2020
Figure 1 for Top-Down Networks: A coarse-to-fine reimagination of CNNs
Figure 2 for Top-Down Networks: A coarse-to-fine reimagination of CNNs
Figure 3 for Top-Down Networks: A coarse-to-fine reimagination of CNNs
Figure 4 for Top-Down Networks: A coarse-to-fine reimagination of CNNs
Viaarxiv icon