The Experts below are selected from a list of 45171 Experts worldwide ranked by ideXlab platform

Daniel Moraru - One of the best experts on this subject based on the ideXlab platform.

  • Multimedia Information Retrieval - Video story Segmentation with multi-modal features: experiments on TRECvid 2003
    Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval - MIR '04, 2004
    Co-Authors: Laurent Besacier, Georges Quénot, Stéphane Ayache, Daniel Moraru
    Abstract:

    This paper describes the first steps of CLIPS/IMAG on the TREC video story Segmentation Task. We mostly describe the multi-modal features used and their respective performance for the story Segmentation Task. These features are based on the audio, video and text modalities. The preliminary system, which has the advantage to be relatively free with respect to the use of training data, is also presented in this paper. First experiments on the TRECVID 2003 evaluation set lead to a recall rate of 0.613 and a precision rate of 0.467. We plan to participate to the official TRECVID 2004 story Segmentation Task with this system

  • Video Story Segmentation with Multi-Modal Features: Experiments on TRECvid 2003
    2004
    Co-Authors: Laurent Besacier, Georges Quénot, Stéphane Ayache, Daniel Moraru
    Abstract:

    This paper describes the first steps of CLIPS/IMAG on the TREC video story Segmentation Task. We mostly describe the multi-modal features used and their respective performance for the story Segmentation Task. These features are based on the audio, video and text modalities. The preliminary system, which has the advantage to be relatively free with respect to the use of training data, is also presented in this paper. First experiments on the TRECVID 2003 evaluation set lead to a recall rate of 0.613 and a precision rate of 0.467.

Daphne Bavelier - One of the best experts on this subject based on the ideXlab platform.

  • The neural correlates of statistical learning in a word Segmentation Task: An fMRI study.
    Brain and Language, 2013
    Co-Authors: Elisabeth A. Karuza, Sarah J. Starling, Madalina E. Tivarus, Elissa L Newport, Richard N Aslin, Daphne Bavelier
    Abstract:

    Functional magnetic resonance imaging (fMRI) was used to assess neural activation as participants learned to segment continuous streams of speech containing syllable sequences varying in their transitional probabilities. Speech streams were presented in four runs, each followed by a behavioral test to measure the extent of learning over time. Behavioral performance indicated that participants could discriminate statistically coherent sequences (words) from less coherent sequences (partwords). Individual rates of learning, defined as the difference in ratings for words and partwords, were used as predictors of neural activation to ask which brain areas showed activity associated with these measures. Results showed significant activity in the pars opercularis and pars triangularis regions of the left inferior frontal gyrus (LIFG). The relationship between these findings and prior work on the neural basis of statistical learning is discussed, and parallels to the frontal/subcortical network involved in other forms of implicit sequence learning are considered.

Laurent Besacier - One of the best experts on this subject based on the ideXlab platform.

  • Multimedia Information Retrieval - Video story Segmentation with multi-modal features: experiments on TRECvid 2003
    Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval - MIR '04, 2004
    Co-Authors: Laurent Besacier, Georges Quénot, Stéphane Ayache, Daniel Moraru
    Abstract:

    This paper describes the first steps of CLIPS/IMAG on the TREC video story Segmentation Task. We mostly describe the multi-modal features used and their respective performance for the story Segmentation Task. These features are based on the audio, video and text modalities. The preliminary system, which has the advantage to be relatively free with respect to the use of training data, is also presented in this paper. First experiments on the TRECVID 2003 evaluation set lead to a recall rate of 0.613 and a precision rate of 0.467. We plan to participate to the official TRECVID 2004 story Segmentation Task with this system

  • Video Story Segmentation with Multi-Modal Features: Experiments on TRECvid 2003
    2004
    Co-Authors: Laurent Besacier, Georges Quénot, Stéphane Ayache, Daniel Moraru
    Abstract:

    This paper describes the first steps of CLIPS/IMAG on the TREC video story Segmentation Task. We mostly describe the multi-modal features used and their respective performance for the story Segmentation Task. These features are based on the audio, video and text modalities. The preliminary system, which has the advantage to be relatively free with respect to the use of training data, is also presented in this paper. First experiments on the TRECVID 2003 evaluation set lead to a recall rate of 0.613 and a precision rate of 0.467.

Federico Tombari - One of the best experts on this subject based on the ideXlab platform.

  • kliep based density ratio estimation for semantically consistent synthetic to real images adaptation in urban traffic scenes
    Intelligent Robots and Systems, 2020
    Co-Authors: Artem Savkin, Federico Tombari
    Abstract:

    Synthetic data has been applied in many deep learning based computer vision Tasks. Limited performance of algorithms trained solely on synthetic data has been approached with domain adaptation techniques such as the ones based on generative adversarial framework. We demonstrate how adversarial training alone can introduce semantic inconsistencies in translated images. To tackle this issue we propose density prematching strategy using KLIEP-based density ratio estimation procedure. Finally, we show that aforementioned strategy improves quality of translated images of underlying method and their usability for the semantic Segmentation Task in the context of autonomous driving.

  • sampling importance resampling for semantically consistent synthetic to real image domain adaptation in urban traffic scenes
    IEEE Intelligent Vehicles Symposium, 2019
    Co-Authors: Artem Savkin, Monika Kasperek, Federico Tombari
    Abstract:

    Synthetic data find application in computer vision Tasks for a long time. Limited performance of algorithms trained solely on synthetic data has been approached with domain adaptation techniques such as the ones based on generative adversarial framework [1]. In this work we demonstrate how using adversarial training alone can introduce semantic inconsistencies in refined images. We suggest leveraging available semantic labels from target domain using naive re-sampling approach alongside with adversarial loss. We also show that aforementioned strategy improves quality of translated images of underlying method and their usability for the semantic Segmentation Task in the context of autonomous driving. This method will be also put in comparison with existing state-of-the-art synthetic to real domain adaptation methods.

Ryosuke Nakamura - One of the best experts on this subject based on the ideXlab platform.

  • Salient object detection on hyperspectral images using features learned from unsupervised Segmentation Task
    arXiv: Computer Vision and Pattern Recognition, 2019
    Co-Authors: Nevrez Imamoglu, Guanqun Ding, Yuming Fang, Asako Kanezaki, Toru Kouyama, Ryosuke Nakamura
    Abstract:

    Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes. However, developments on hyperspectral imaging systems enable us to obtain redundant spectral information of the observed scenes from the reflected light source from objects. A few studies using low-level features on hyperspectral images demonstrated that salient object detection can be achieved. In this work, we proposed a salient object detection model on hyperspectral images by applying manifold ranking (MR) on self-supervised Convolutional Neural Network (CNN) features (high-level features) from unsupervised image Segmentation Task. Self-supervision of CNN continues until clustering loss or saliency maps converges to a defined error between each iteration. Finally, saliency estimations is done as the saliency map at last iteration when the self-supervision procedure terminates with convergence. Experimental evaluations demonstrated that proposed saliency detection algorithm on hyperspectral images is outperforming state-of-the-arts hyperspectral saliency models including the original MR based saliency model.

  • ICASSP - Salient Object Detection on Hyperspectral Images Using Features Learned from Unsupervised Segmentation Task
    ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019
    Co-Authors: Nevrez Imamoglu, Guanqun Ding, Yuming Fang, Asako Kanezaki, Toru Kouyama, Ryosuke Nakamura
    Abstract:

    Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes. However, developments on hyperspectral imaging systems enable us to obtain redundant spectral information of the observed scenes from the reflected light source from objects. A few studies using low-level features on hyper-spectral images demonstrated that salient object detection can be achieved. In this work, we proposed a salient object detection model on hyperspectral images by applying manifold ranking (MR) on self-supervised Convolutional Neural Network (CNN) features (high-level features) from unsupervised image Segmentation Task. Self-supervision of CNN continues until clustering loss or saliency maps converges to a defined error between each iteration. Finally, saliency estimations is done as the saliency map at last iteration when the self-supervision procedure terminates with convergence. Experimental evaluations demonstrated that proposed saliency detection algorithm on hyperspectral images is outperforming state-of-the-arts hyperspectral saliency models including the original MR based saliency model.