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Dilated Convolution with Learnable Spacings makes visual models more aligned with humans: a Grad-CAM study
Rabih Chamas, Ismail Khalfaoui-Hassani, Timothee Masquelier
Dilated Convolution with Learnable Spacing (DCLS) is a recent advanced convolution method that allows enlarging the receptive fields (RF) without increasing the number of parameter…
Dilated Convolution with Learnable Spacings: beyond bilinear interpolation
Ismail Khalfaoui-Hassani, Thomas Pellegrini, Timothée Masquelier
Dilated Convolution with Learnable Spacings (DCLS) is a recently proposed variation of the dilated convolution in which the spacings between the non-zero elements in the kernel, or…
Optical flow estimation from event-based cameras and spiking neural networks
Javier Cuadrado, Ulysse Rançon, Benoît Cottereau +2
Event-based cameras are raising interest within the computer vision community. These sensors operate with asynchronous pixels, emitting events, or "spikes", when the luminance chan…
Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharing
Alireza Azadbakht, Saeed Reza Kheradpisheh, Ismail Khalfaoui-Hassani +1
Deep convolutional neural networks (DCNNs) have become the state-of-the-art (SOTA) approach for many computer vision tasks: image classification, object detection, semantic segment…
Dilated convolution with learnable spacings
Ismail Khalfaoui-Hassani, Thomas Pellegrini, Timothée Masquelier
Recent works indicate that convolutional neural networks (CNN) need large receptive fields (RF) to compete with visual transformers and their attention mechanism. In CNNs, RFs can…
StereoSpike: Depth Learning with a Spiking Neural Network
Ulysse Rançon, Javier Cuadrado-Anibarro, Benoit R. Cottereau +1
Depth estimation is an important computer vision task, useful in particular for navigation in autonomous vehicles, or for object manipulation in robotics. Here we solved it using a…
Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks
Milad Mozafari, Mohammad Ganjtabesh, Abbas Nowzari-Dalini +2
The primate visual system has inspired the development of deep artificial neural networks, which have revolutionized the computer vision domain. Yet these networks are much less en…
STDP-based spiking deep convolutional neural networks for object recognition
Saeed Reza Kheradpisheh, Mohammad Ganjtabesh, Simon J Thorpe +1
Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity…
Humans and deep networks largely agree on which kinds of variation make object recognition harder
Saeed Reza Kheradpisheh, Masoud Ghodrati, Mohammad Ganjtabesh +1
View-invariant object recognition is a challenging problem, which has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notor…
Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition
Saeed Reza Kheradpisheh, Masoud Ghodrati, Mohammad Ganjtabesh +1
Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases.…
Bio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition
Saeed Reza Kheradpisheh, Mohammad Ganjtabesh, Timothée Masquelier
Retinal image of surrounding objects varies tremendously due to the changes in position, size, pose, illumination condition, background context, occlusion, noise, and nonrigid defo…