Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains

This paper proposes an adaptive graph-based approach for multi-label image classification. Graph-based methods have been largely exploited in the field of multi-label classification, given their ability to model label correlations. Specifically, their effectiveness has been proven not only when considering a single domain but also when taking into account multiple domains. However, the topology of the used graph is not optimal as it is pre-defined heuristically. In addition, consecutive Graph Convolutional Network (GCN) aggregations tend to destroy the feature similarity. To overcome these issues, an architecture for learning the graph connectivity in an end-to-end fashion is introduced. This is done by integrating an attention-based mechanism and a similarity-preserving strategy. The proposed framework is then extended to multiple domains using an adversarial training scheme. Numerous experiments are reported on well-known single-domain and multi-domain benchmarks. The results demonstrate that our approach achieves competitive results in terms of mean Average Precision (mAP) and model size as compared to the state-of-the-art. The code will be made publicly available

Fast Adaptive Reparametrization (FAR) with Application to Human Action Recognition

In this paper, a fast approach for curve reparametrization, called Fast Adaptive Reparamterization (FAR), is introduced. Instead of computing an optimal matching between two curves such as Dynamic Time Warping (DTW) and elastic distance-based approaches, our method is applied to each curve independently, leading to linear computational complexity. It is based on a simple replacement of the curve parameter by a variable invariant under specific variations of reparametrization. The choice of this variable is heuristically made according to the application of interest. In addition to being fast, the proposed reparametrization can be applied not only to curves observed in Euclidean spaces but also to feature curves living in Riemannian spaces. To validate our approach, we apply it to the scenario of human action recognition using curves living in the Riemannian product Special Euclidean space SE(3) n. The obtained results on three benchmarks for human action recognition (MSRAction3D, Florence3D, and UTKinect) show that our approach competes with state-of-the-art methods in terms of accuracy and computational cost

Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification

In this paper, a discriminator-free adversarial-based Unsupervised Domain Adaptation (UDA) for Multi-Label Image Classification (MLIC) referred to as DDA-MLIC is proposed. Over the last two years, some attempts have been made for introducing adversarial-based UDA methods in the context of MLIC. However, these methods which rely on an additional discriminator subnet present two shortcomings. First, the learning of domain-invariant features may harm their task-specific discriminative power, since the classification and discrimination tasks are decoupled. Moreover, the use of an additional discriminator usually induces an increase of the network size. Herein, we propose to overcome these issues by introducing a novel adversarial critic that is directly deduced from the task-specific classifier. Specifically, a two-component Gaussian Mixture Model (GMM) is fitted on the source and target predictions, allowing the distinction of two clusters. This allows extracting a Gaussian distribution for each component. The resulting Gaussian distributions are then used for formulating an adversarial loss based on a Frechet distance. The proposed method is evaluated on three multi-label image datasets. The obtained results demonstrate that DDA-MLIC outperforms existing state-of-the-art methods while requiring a lower number of parameters

Vers une reconnaissance en ligne d'actions à partir de caméras RGB-D

International audienc

An extension of kernel learning methods using a modified Log-Euclidean distance for fast and accurate skeleton-based Human Action Recognition

International audienc

Kinematic Spline Curves: A temporal invariant descriptor for fast action recognition

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3D real-time human action recognition using a spline interpolation approach

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A fast and accurate motion descriptor for human action recognition applications

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Validation d'un Nouveau Modèle Statistique de Scapula Augmenté de Marqueurs Anatomiques

International audienceCe papier décrit la validation d'un modèle statistique de scapula (SSM) augmenté d'un ensemble de marqueurs anatomiques ayant un intérêt clinique. Le SSM utilisé est issu de nos récents travaux ayant abouti à la publication d'un des premiers modèles statistiques de l'os scapulaire chez l'humain adulte. En effet, la scapula est une forme 3D difficile à modéliser statistiquement du fait de sa forme complexe et de sa grande variabilité. Ce SSM avait été validé par les critères classiques de robustesse de construction du SSM à savoir, compacité, généralité et spécificité. Cependant, la robustesse de la représentation statistique n'est pas garante de sa validité anatomique pourtant primordiale pour des applications cliniques. Dans cette étude, nous présentons une nouvelle méthode pour l'ajout d'informations anatomiques dans le SSM développé et nous l'évaluons par un processus de sélection des marqueurs anatomiques utilisant un groupe mixte d'observateurs. Nous obtenons d'excellents résultats issus des analyses de variance intra et inter-observateurs. Ces résultats nous permettent d'envisager l'utilisation de ce SSM augmenté pour des applications de segmentation automatique d'IRM et des études biomécaniques du complexe de l'épaule