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Found 11 papers, 4 papers with code
MobileNetV4 - Universal Models for the Mobile Ecosystem
We present the latest generation of MobileNets, known as MobileNetV4 (MNv4), featuring universally efficient architecture designs for mobile devices.
Exploiting Invariance in Training Deep Neural Networks
Inspired by two basic mechanisms in animal visual systems, we introduce a feature transform technique that imposes invariance properties in the training of deep neural networks.
Network Deconvolution
Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies a kernel to overlapping regions shifted across the image.
EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras
In addition to camera egomotion and a dense depth map, the network estimates pixel-wise independently moving object segmentation and computes per-object 3D translational velocities for moving objects.
Unsupervised Learning of Dense Optical Flow, Depth and Egomotion from Sparse Event Data
In this work we present a lightweight, unsupervised learning pipeline for \textit{dense} depth, optical flow and egomotion estimation from sparse event output of the Dynamic Vision Sensor (DVS).
Evenly Cascaded Convolutional Networks
We introduce Evenly Cascaded convolutional Network (ECN), a neural network taking inspiration from the cascade algorithm of wavelet analysis.
On the Importance of Consistency in Training Deep Neural Networks
We conclude this paper with the construction of a novel contractive neural network.
LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning
LightNet is a lightweight, versatile and purely Matlab-based deep learning framework.
What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots
For robots that have the capability to interact with the physical environment through their end effectors, understanding the surrounding scenes is not merely a task of image classification or object recognition.
Sparse Norm Filtering
Optimization-based filtering smoothes an image by minimizing a fidelity function and simultaneously preserves edges by exploiting a sparse norm penalty over gradients.
Spectral Graph Cut from a Filtering Point of View
In this paper, we analyze image segmentation algorithms that are based on spectral graph theory, e. g., normalized cut, and show that there is a natural connection between spectural graph theory based image segmentationand and edge preserving filtering.
