visiir.univ-lr.fr

VISIIR

The VISIIR project proposes new interactive methods for providing powerful semantic annotation systems. The originality of the proposal is three-fold:. Eye-tracker-driven system. A major specificity of the project is to use the latest eye-tracker. Techniques for validating and improving the vision and learning models developed by the academic partners. New paradigm for visual representation and learning. We introduce a novel learning scheme. Combining supervised and interactive methods.

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VISIIR | visiir.univ-lr.fr Reviews

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The VISIIR project proposes new interactive methods for providing powerful semantic annotation systems. The originality of the proposal is three-fold:. Eye-tracker-driven system. A major specificity of the project is to use the latest eye-tracker. Techniques for validating and improving the vision and learning models developed by the academic partners. New paradigm for visual representation and learning. We introduce a novel learning scheme. Combining supervised and interactive methods.

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4 tasks and deliverables

5 results

6 publication

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VISIIR | visiir.univ-lr.fr Reviews

https://visiir.univ-lr.fr

The VISIIR project proposes new interactive methods for providing powerful semantic annotation systems. The originality of the proposal is three-fold:. Eye-tracker-driven system. A major specificity of the project is to use the latest eye-tracker. Techniques for validating and improving the vision and learning models developed by the academic partners. New paradigm for visual representation and learning. We introduce a novel learning scheme. Combining supervised and interactive methods.

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visiir.univ-lr.fr

1

Tasks & Deliverables

http://visiir.univ-lr.fr/index.php/tasks-deliverables

Task 1 Unsupervised Bio-Inspired Image Representations. D11 (T0 18): State-of-the-art report on saliency models and biologically-inspired networks. [ PDF. D12 (T0 18): Technical report code on new saliency inspired image descriptors. [ PDF. D13 (T0 18): Technical report code on the hybrid biologically-inspired method. [ PDF. Task 2 Interactive Eye-driven Learning System. D21 (T0 18): State-of-the-art report on representation learning and instance-basedmethods including eye-tracker. [ PDF. Report on scala...

2

News

http://visiir.univ-lr.fr/index.php/news

23 Nov 2016, reports (D2.3, D2.4, D3.2) handed to ANR. 30 May 2016, technical meeting in Paris:. Advancement of Stefanie, I3S, [ Slides. Advancement of Thomas, LIP6, [ Slides. Mid-term evaluation of Xin, LIP6, [ Slides. 9 May 2016, LIP6 paper accepted as an oral presentation at IEEE ICIP 2016, [ Paper. 29 Mars 2016, valorization day of projet VISIIR, Paris. Presentation, [ PDF. 23 November 2015, mid-term evaluation succeeded in ANR, Paris. General presentation, foucus LIP6, [ PDF. 10 May 2015, LIP6 paper.

3

Publication

http://visiir.univ-lr.fr/index.php/publication

X Wang, N. Thome, M. Cord, Gaze Latent SVM for image classification, IEEE ICIP, 2016 [ Paper. Patrice Denis et al. Improvement of natural image. Search engines results by emotional filtering. Submission to EAI Transactions on Creative Technologies. X Wang, D. Kumar, N. Thome, M. Cord, F. Precioso. Recipe Recognition with Large Multimodal Food Dataset, Workshop Cooking and Eating Activities, IEEE ICME 2015 [ Paper. S LOPEZ, A. REVEL, D. LINGRAND, F. PRECIOSO, One gaze is.

4

Results

http://visiir.univ-lr.fr/index.php/results

Prototype mobile food recipe categorization application of LIP6. Link: http:/ visiir.lip6.fr/. I3S eye tracking data collected in collaboration with ergonomists.

5

Consortium

http://visiir.univ-lr.fr/index.php/consortium

The consortium is composed by 3 academic partners and 1 industrial partner:. LIP6 - Université Pierre et Marie CURIE. I3S - Université Nice Sophia Antipolis. L3I - Université de La Rochelle. Contact: This email address is being protected from spambots. You need JavaScript enabled to view it. Contact: This email address is being protected from spambots. You need JavaScript enabled to view it.

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thomas-robert.fr

Thomas Robert | Resume

http://www.thomas-robert.fr/resume

Hello, I'm Thomas Robert. I’m a research engineer in Deep Learning working at the LIP6 lab (University Pierre and Marie Curie, Paris) and future PhD student starting in Fall 2016. I am 24 years old. And have a driving license. I am working at the Pierre and Marie Curie University. In Paris, at the LIP6 lab. In the MLIA team. Managed by Patrick Gallinari. And under the supervision of Matthieu Cord. Example of publications by Matthieu Cord's team:. T Durand, N Thome, M Cord. T Durand, N Thome, M Cord.

thomas-robert.fr

Thomas Robert | Resume

http://www.thomas-robert.fr/en/resume

Hello, I'm Thomas Robert. I’m a research engineer in Deep Learning working at the LIP6 lab (University Pierre and Marie Curie, Paris) and future PhD student starting in Fall 2016. I am 24 years old. And have a driving license. I am working at the Pierre and Marie Curie University. In Paris, at the LIP6 lab. In the MLIA team. Managed by Patrick Gallinari. And under the supervision of Matthieu Cord. Example of publications by Matthieu Cord's team:. T Durand, N Thome, M Cord. T Durand, N Thome, M Cord.

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VISIIR

VIsual Seek for Interactive Image Retrieval. Click here to select or take a picture of a meal. This demo uses a deep convolutional neural network (CNN) to recognize food images across 101 categories. This CNN follows the architecture of the OverFeat CNN (Sermanet et al. 2013). It has been initialized with the weights of the network trained on the ImageNet dataset and was then re-trained (fine-tuned) on our own dataset for food recognition. Read the scientific publication. The VISIIR project proposes new ...

visiir.univ-lr.fr

VISIIR

The VISIIR project proposes new interactive methods for providing powerful semantic annotation systems. The originality of the proposal is three-fold:. Eye-tracker-driven system. A major specificity of the project is to use the latest eye-tracker. Techniques for validating and improving the vision and learning models developed by the academic partners. New paradigm for visual representation and learning. We introduce a novel learning scheme. Combining supervised and interactive methods.

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