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Publications associées (55)

Self-Supervision By Prediction For Object Discovery In Videos

Despite their irresistible success, deep learning algorithms still heavily rely on annotated data, and unsupervised settings pose many challenges, such as finding the right inductive bias in diverse scenarios. In this paper, we propose an object-centric mo ...

IEEE2021

Geometry-Aware Deep Recurrent Neural Networks for Hyperspectral Image Classification

Mathieu Salzmann, Wei Wang, Siyuan Hao

Variants of deep networks have been widely used for hyperspectral image (HSI)-classification tasks. Among them, in recent years, recurrent neural networks (RNNs) have attracted considerable attention in the remote sensing community. However, complex geomet ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Learning and leveraging shared domain semantics to counteract visual domain shifts

Róger Bermúdez Chacón

One of the main limitations of artificial intelligence today is its inability to adapt to unforeseen circumstances. Machine Learning (ML), due to its data-driven nature, is particularly susceptible to this. ML relies on observations in order to learn impli ...

EPFL2020

Domain Adaptation and Investigation of Robustness of DNN-based Embeddings for Text-Independent Speaker Verification Using Dilated Residual Networks

Sébastien Marcel

Robustness of extracted embeddings in cross-database scenarios is one of the main challenges in text-independent speaker verification (SV) systems. In this paper, we investigate this robustness via performing structural cross-database experiments with or w ...

Idiap2019

Audio Feature Extraction with Convolutional Neural Autoencoders with Application to Voice Conversion

Golnooshsadat Elhami

Feature extraction is a key step in many machine learning and signal processing applications. For speech signals in particular, it is important to derive features that contain both the vocal characteristics of the speaker and the content of the speech. In ...

2019

DeepFly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila

Pascal Fua, Pavan P Ramdya, Helge Jochen Rhodin, Semih Günel, Daniel Eduardo Morales Garza, João Henrique Campagnolo

Studying how neural circuits orchestrate limbed behaviors requires the precise measurement of the positions of each appendage in 3-dimensional (3D) space. Deep neural networks can estimate 2-dimensional (2D) pose in freely behaving and tethered animals. Ho ...

2019

Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis

Jean-Philippe Thiran, Hazim Kemal Ekenel, Mohammad Saeed Rad

Cross-domain synthesizing realistic faces to learn deep models has attracted increasing attention for facial expression analysis as it helps to improve the performance of expression recognition accuracy despite having small number of real training images. ...

IEEE2019

An improved objective metric to predict image quality using deep neural networks

Touradj Ebrahimi, Pinar Akyazi

Objective quality assessment of compressed images is very useful in many applications. In this paper we present an objective quality metric that is better tuned to evaluate the quality of images distorted by compression artifacts. A deep convolutional neur ...

2019

Overcoming Multi-model Forgetting

Mathieu Salzmann, Anthony Christopher Davison, Martin Jaggi, Yassine Benyahia, Claudiu-Cristian Musat, Kaicheng Yu

We identify a phenomenon, which we refer to as multi-model forgetting, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, ...

JMLR2019

How to Tell Ancient Signs Apart? Recognizing and Visualizing Maya Glyphs with CNNs

Daniel Gatica-Perez, Jean-Marc Odobez, Gülcan Can

Thanks to the digital preservation of cultural heritage materials, multimedia tools (e.g., based on automatic visual processing) considerably ease the work of scholars in the humanities and help them to perform quantitative analysis of their data. In this ...

2018

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