Publications associées à Compression artifact

Towards super resolution in the compressed domain of learning-based image codecs

Touradj Ebrahimi, Evgeniy Upenik, Michela Testolina

Learning-based image coding has shown promising results during recent years. Unlike the traditional approaches to image compression, learning-based codecs exploit deep neural networks for reducing dimensionality of the input at the stage where a linear tra ...

SPIE2021

Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformers

Jean-Marc Odobez, Michael Villamizar

We propose to leverage Transformer architectures for non-autoregressive human motion prediction. Our approach decodes elements in parallel from a query sequence, instead of conditioning on previous predictions such as in state-of-the-art RNN-based approach ...

IEEE COMPUTER SOC2021

Oligomodal metamaterials with multifunctional mechanics

Aleksi Antoine Bossart

Mechanical metamaterials are artificial composites that exhibit a wide range of advanced functionalities such as negative Poisson's ratio, shape shifting, topological protection, multistability, extreme strength-to-density ratio, and enhanced energy dissip ...

NATL ACAD SCIENCES2021

Learning-based Image Coding: Early Solutions Reviewing and Subjective Quality Evaluation

Touradj Ebrahimi, Pinar Akyazi

Nowadays, image and video are the data types that consume most of the resources of modern communication channels, both in fixed and wireless networks. Thus, it is vital to compress visual data as much as possible, while maintaining some target quality leve ...

SPIE-INT SOC OPTICAL ENGINEERING2021

What determines the temporal extent of unconscious feature integration?

Michael Herzog, Leila Drissi Daoudi - Kleinbauer, Lukas Vogelsang

Visual features are spatiotemporally integrated along motion trajectories. For instance, when a central line is followed by pairs of flanking lines, two motion streams diverging from the center are perceived. The central line is rendered unconscious by the ...

2021

Partition and Code: learning how to compress graphs

Michael Bronstein, Andreas Loukas, Nikolaos Karalias, Georgios Bouritsas

Can we use machine learning to compress graph data? The absence of ordering in graphs poses a significant challenge to conventional compression algorithms, limiting their attainable gains as well as their ability to discover relevant patterns. On the other ...

2021

Direction is what you need: Improving Word Embedding Compression in Large Language Models

Karl Aberer, Rémi Philippe Lebret, Mohammadreza Banaei

The adoption of Transformer-based models in natural language processing (NLP) has led to great success using a massive number of parameters. However, due to deployment constraints in edge devices, there has been a rising interest in the compression of thes ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Towards image denoising in the latent space of learning-based compression

Touradj Ebrahimi, Evgeniy Upenik, Michela Testolina

In recent years, learning-based image compression has demonstrated similar or superior performance when com- pared to conventional approaches in terms of compression efficiency and visual quality. Typically, learning-based image compression takes advantage ...

SPIE2021

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Compression artifact

Towards super resolution in the compressed domain of learning-based image codecs

Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformers

Oligomodal metamaterials with multifunctional mechanics

Learning-based Image Coding: Early Solutions Reviewing and Subjective Quality Evaluation

What determines the temporal extent of unconscious feature integration?

Partition and Code: learning how to compress graphs

Advances In Morphological Neural Networks: Training, Pruning And Enforcing Shape Constraints

Direction is what you need: Improving Word Embedding Compression in Large Language Models

Ultrasound Imaging: From Physical Modeling to Deep Learning

Towards image denoising in the latent space of learning-based compression

Graph Chatbot

Chattez avec Graph Search

Direction is what you need: Improving Word Embedding Compression in Large Language Models

Oligomodal metamaterials with multifunctional mechanics

Towards super resolution in the compressed domain of learning-based image codecs

Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformers

Learning-based Image Coding: Early Solutions Reviewing and Subjective Quality Evaluation

What determines the temporal extent of unconscious feature integration?

Advances In Morphological Neural Networks: Training, Pruning And Enforcing Shape Constraints

Partition and Code: learning how to compress graphs

Ultrasound Imaging: From Physical Modeling to Deep Learning

Towards image denoising in the latent space of learning-based compression