We are considering cable-driven parallel robot (CDPR), where the legs of the robot are constituted of cables that can be independently coiled/uncoiled. We show that whatever the size of the CDPR is we may have slack cables so that using a sagging cable model that takes into account both the mass and elasticity of the cables will improve the positio...
Supervised training has led to state-of-the-art results in image and video denoising. However, its application to real data is limited since it requires large datasets of noisy-clean pairs that are difficult to obtain. For this reason, networks are often trained on realistic synthetic data. More recently, some self-supervised frameworks have been p...
Prediction of head movements in immersive media is key to design efficient streaming systems able to focus the bandwidth budget on visible areas of the content. Numerous proposals have therefore been made in the recent years to predict 360°images and videos. However, the performance of these models is limited by a main characteristic of the head mo...
Intensive information analysis related to a ship and its environment is required in order to make the appropriate decisions during naval missions. However, human capabilities are no longer sufficient to reliably and rapidly process the massive amount of heterogeneous data collected by a huge lot of different sensors. That is the reason why Artifici...
Deep learning based pipelines for semantic segmentation often ignore structural information available on annotated images used for training. We propose a novel post-processing module enforcing structural knowledge about the objects of interest to improve segmentation results provided by deep learning. This module corresponds to a ``many-to-one-or-n...
The paper addresses the fundamental task of semantic image analysis by exploiting structural information (spatial relationshipsbetween image regions). We propose to combine a deep neural network(CNN) with graph matching where graphs encode efficiently structuralinformation related to regions segmented by the CNN. Our novel approach solves the quadr...
Published in Evolutionary computation
Several test function suites are being used for numerical benchmarking of multiobjective optimization algorithms. While they have some desirable properties, such as well-understood Pareto sets and Pareto fronts of various shapes, most of the currently used functions possess characteristics that are arguably underrepresented in real-world problems s...
Due to the black-box nature of deep learning models, there is a recent development of solutions for visual explanations of CNNs. Given the high cost of user studies, metrics are necessary to compare and evaluate these different methods. In this paper, we critically analyze the Deletion Area Under Curve (DAUC) and Insertion Area Under Curve (IAUC) m...
In this paper, we tackle the problem of finding potentially problematic samples and complex regions of the input space for large pools of data without any supervision, with the objective of being relayed to and validated by a domain expert. This information can be critical, as even a low level of noise in the dataset may severely bias the model thr...
Published in IEEE transactions on neural networks and learning systems
In this article, we propose a novel architecture called hierarchical-task reservoir (HTR) suitable for real-time applications for which different levels of abstraction are available. We apply it to semantic role labeling (SRL) based on continuous speech recognition. Taking inspiration from the brain, this demonstrates the hierarchies of representat...
