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Megapixel single-photon avalanche diode (SPAD) arrays have been developed recently, opening up the possibility of deploying SPADs as general-purpose passive cameras for photography and computer vision. However, most previous work on SPADs has been limited ...

The recent developments of deep learning cover a wide variety of tasks such as image classification, text translation, playing go, and folding proteins. All these successful methods depend on a gradient-based learning algorithm to train a model on massive ...

This paper introduces TACOSS a text-image alignment approach that allows explainable land cover semantic segmentation by directly integrating semantic concepts encoded from texts. TACOSS combines convolutional neural networks for visual feature extraction ...

Visual data play a crucial role in modern society, and the rate at which images and videos are acquired, stored, and exchanged every day is rapidly increasing. Image compression is the key technology that enables storing and sharing of visual content in an ...

In this thesis, we explore techniques for addressing the communication bottleneck in data-parallel distributed training of deep learning models. We investigate algorithms that either reduce the size of the messages that are exchanged between workers, or th ...

Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional n ...

The usual explanation of the efficacy of wavelet-based methods hinges on the sparsity of many real-world objects in the wavelet domain. Yet, standard wavelet-shrinkage techniques for sparse reconstruction are not competitive in practice, one reason being t ...

Data augmentation has proven its usefulness to improve model generalization and performance. While it is commonly applied in computer vision application when it comes to multi-view systems, it is rarely used. Indeed geometric data augmentation can break th ...

Due to the increasing number of pictures captured and stored every day by and on digital devices, lossy image compression has become inevitable to limit the needed storage requirement. As a consequence, these compression methods might introduce some visual ...

Attribute-based representations help machine learning models perform tasks based on human understandable concepts, allowing a closer human-machine collaboration. However, learning attributes that accurately reflect the content of an image is not always str ...

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Chattez avec Graph Search

Seeing Photons in Color

Transformer Models for Vision

Text as a Richer Source of Supervision in Semantic Segmentation Tasks

JPEG AIC-3 Dataset: Towards Defining the High Quality to Nearly Visually Lossless Quality Range

Communication-efficient distributed training of machine learning models

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation

The Sparsity of Cycle Spinning for Wavelet-Based Solutions of Linear Inverse Problems

Two-level Data Augmentation for Calibrated Multi-view Detection

Towards JPEG AIC part 3: visual quality assessment of high to visually lossless image coding

Attribute Prediction as Multiple Instance Learning