72 citations · 266 across the 18 of their papers we have counts for
19 papers
Identifying Surgical Instruments in Laparoscopy Using Deep Learning Instance Segmentation
Sabrina Kletz, Klaus Schoeffmann, Jenny Benois-Pineau +1
Recorded videos from surgeries have become an increasingly important information source for the field of medical endoscopy, since the recorded footage shows every single detail of…
Sport Task: Fine Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2022
Pierre-Etienne Martin, Jordan Calandre, Boris Mansencal +4
Sports video analysis is a widespread research topic. Its applications are very diverse, like events detection during a match, video summary, or fine-grained movement analysis of a…
3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition
Pierre-Etienne Martin, J Benois-Pineau, R Péteri +2
3D convolutional networks is a good means to perform tasks such as video segmentation into coherent spatio-temporal chunks and classification of them with regard to a target taxono…
Sports Video: Fine-Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2021
Pierre-Etienne Martin, Jordan Calandre, Boris Mansencal +4
Sports video analysis is a prevalent research topic due to the variety of application areas, ranging from multimedia intelligent devices with user-tailored digests up to analysis o…
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…
White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks
Meghna P Ayyar, Jenny Benois-Pineau, Akka Zemmari
In recent years, deep learning has become prevalent to solve applications from multiple domains. Convolutional Neural Networks (CNNs) particularly have demonstrated state of the ar…
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…
Move-to-Data: A new Continual Learning approach with Deep CNNs, Application for image-class recognition
Miltiadis Poursanidis, Jenny Benois-Pineau, Akka Zemmari +2
In many real-life tasks of application of supervised learning approaches, all the training data are not available at the same time. The examples are lifelong image classification o…
Improvements of Motion Estimation and Coding using Neural Networks
Raz Birman, Yoram Segal, Ofer Hadar +1
Inter-Prediction is used effectively in multiple standards, including H.264 and HEVC (also known as H.265). It leverages correlation between blocks of consecutive video frames in o…
3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnostics
Alexander Khvostikov, Karim Aderghal, Andrey Krylov +2
In the last decade, computer-aided early diagnostics of Alzheimer's Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research…
3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies
Alexander Khvostikov, Karim Aderghal, Jenny Benois-Pineau +2
Computer-aided early diagnosis of Alzheimers Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research in recent years. Some…
Saliency Driven Object recognition in egocentric videos with deep CNN
Philippe Pérez de San Roman, Jenny Benois-Pineau, Jean-Philippe Domenger +3
The problem of object recognition in natural scenes has been recently successfully addressed with Deep Convolutional Neuronal Networks giving a significant break-through in recogni…
Deep Learning for Saliency Prediction in Natural Video
Souad Chaabouni, Jenny Benois-Pineau, Ofer Hadar +1
The purpose of this paper is the detection of salient areas in natural video by using the new deep learning techniques. Salient patches in video frames are predicted first. Then th…
Egocentric vision IT technologies for Alzheimer disease assessment and studies
Hugo Boujut, Vincent Buso, Guillaume Bourmaud +5
Egocentric vision technology consists in capturing the actions of persons from their own visual point of view using wearable camera sensors. We apply this new paradigm to instrumen…
Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of Dementia
Svebor Karaman, Jenny Benois-Pineau, Vladislavs Dovgalecs +5
This paper presents a method for indexing activities of daily living in videos obtained from wearable cameras. In the context of dementia diagnosis by doctors, the videos are recor…
Multi-Layer Local Graph Words for Object Recognition
Svebor Karaman, Jenny Benois-Pineau, Rémi Mégret +1
In this paper, we propose a new multi-layer structural approach for the task of object based image retrieval. In our work we tackle the problem of structural organization of local…
Activities of Daily Living Indexing by Hierarchical HMM for Dementia Diagnostics
Svebor Karaman, Jenny Benois-Pineau, Jean-François Dartigues +3
This paper presents a method for indexing human ac- tivities in videos captured from a wearable camera being worn by patients, for studies of progression of the dementia diseases.…
Nested Graph Words for Object Recognition
Svebor Karaman, Jenny Benois-Pineau, Rémi Mégret
In this paper, we propose a new, scalable approach for the task of object based image search or object recognition. Despite the very large literature existing on the scalability is…
Human Daily Activities Indexing in Videos from Wearable Cameras for Monitoring of Patients with Dementia Diseases
Svebor Karaman, Jenny Benois-Pineau, Rémi Mégret +3
Our research focuses on analysing human activities according to a known behaviorist scenario, in case of noisy and high dimensional collected data. The data come from the monitorin…