output
20042024
most citedHigh-Order Synchrosqueezing Transform for Multicomponent Signals Analysis -- With an Application to Gravitational-Wave Signal

518 citations

Showing cs.CVShow all

11 papers · 1 filter

cs.CV20237 cited

Evaluation of Explanation Methods of AI -- CNNs in Image Classification Tasks with Reference-based and No-reference Metrics

A. Zhukov, J. Benois-Pineau, R. Giot

The most popular methods in AI-machine learning paradigm are mainly black boxes. This is why explanation of AI decisions is of emergency. Although dedicated explanation tools have…

cs.CV202113 cited

Three-Stream 3D/1D CNN for Fine-Grained Action Classification and Segmentation in Table Tennis

Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Péteri +1

This paper proposes a fusion method of modalities extracted from video through a three-stream network with spatio-temporal and temporal convolutions for fine-grained action classif…

cs.CV20204 cited

Natural vs Balanced Distribution in Deep Learning on Whole Slide Images for Cancer Detection

Ismat Ara Reshma, Sylvain Cussat-Blanc, Radu Tudor Ionescu +2

The class distribution of data is one of the factors that regulates the performance of machine learning models. However, investigations on the impact of different distributions ava…

cs.CV2020

3D attention mechanism for fine-grained classification of table tennis strokes using a Twin Spatio-Temporal Convolutional Neural Networks

Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Péteri +1

The paper addresses the problem of recognition of actions in video with low inter-class variability such as Table Tennis strokes. Two stream, "twin" convolutional neural networks a…

cs.CV20193 cited

Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention

Vivien Sainte Fare Garnot, Loic Landrieu, Sebastien Giordano +1

Satellite image time series, bolstered by their growing availability, are at the forefront of an extensive effort towards automated Earth monitoring by international institutions.…

cs.CV201772 cited

Cost-Effective Active Learning for Melanoma Segmentation

Marc Gorriz, Axel Carlier, Emmanuel Faure +1

We propose a novel Active Learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of trai…