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transverse [2019/10/07 09:41]
cani [List of available projects for 2019-2020]
transverse [2019/10/07 14:09] (current)
cani [List of available projects for 2019-2020]
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-**Google** - contact Damien Henry <​damienhenry@google.com>​+**Google ​(contact Damien Henry <​damienhenry@google.com>​)** 
  
  
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 * **Google 2 : Augmented art-selfy**  * **Google 2 : Augmented art-selfy**
  
-Could we render your own face within the art piece? +How can we make Art Selfies look closer to the user's face: Could we render your own face within the art piece? Could we warp the art piece so that it matches better your photo? ​
- +
-Could we warp the art piece so that it matches better your photo?+
  
 **Idemia** - contact LANNES Sarah <​sarah.lannes@idemia.com>​ **Idemia** - contact LANNES Sarah <​sarah.lannes@idemia.com>​
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 **Inria** - contact TBC **Inria** - contact TBC
  
-* **Inria 1 : Title: Can we teach computers to draw and read plots? Contact Adrien Boursseau, Inria sophia-antipolis,​ Adrien.Bousseau@inria.fr ** +* **Inria 1 : Title: Can we teach computers to draw and read plots? ​(Contact Adrien Boursseau, Inria sophia-antipolis, ​<Adrien.Bousseau@inria.fr>) **
  
 Topic: Designing effective infographics,​ such as data plots, is a challenging task because complex information needs to be conveyed with simple 2D shapes (dots, lines, rectangles...). Conversely, understanding a plot requires making sense of individual shapes as well as their relationships. The goal of this project is to attempt to teach an AI agent to draw and read simple plots. We will take inspiration from methods originally designed for machine translation and image captioning, and cast the problem of generating a plot as the problem of translating a data table into an image, and the problem of interpreting a plot as translating an image into a data table. We hope to solve these two problems jointly such that the agent learns to generate plots that, once interpreted,​ minimize loss of information. ​ Topic: Designing effective infographics,​ such as data plots, is a challenging task because complex information needs to be conveyed with simple 2D shapes (dots, lines, rectangles...). Conversely, understanding a plot requires making sense of individual shapes as well as their relationships. The goal of this project is to attempt to teach an AI agent to draw and read simple plots. We will take inspiration from methods originally designed for machine translation and image captioning, and cast the problem of generating a plot as the problem of translating a data table into an image, and the problem of interpreting a plot as translating an image into a data table. We hope to solve these two problems jointly such that the agent learns to generate plots that, once interpreted,​ minimize loss of information. ​
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     Contexte : le yielding est une pratique issue du milieu de l'​aérien et de l'​hôtellerie,​ consistant à faire évoluer le prix d'une prestation en fonction de la saison. L'​interim étant fortement soumis à la saisonnalité (période des fêtes notamment), Gojob souhaite expérimenter l'​apport de cette pratique à son modèle économique. ​     Contexte : le yielding est une pratique issue du milieu de l'​aérien et de l'​hôtellerie,​ consistant à faire évoluer le prix d'une prestation en fonction de la saison. L'​interim étant fortement soumis à la saisonnalité (période des fêtes notamment), Gojob souhaite expérimenter l'​apport de cette pratique à son modèle économique. ​
     ​     ​
-* **Ynsect : insect species identification**+* **Ynsect : insect species identification ​(contact Fabrice Berro <​fbe@ynsect.com>​)**
  
-To be defined+From photos taken in an insect farm, detect the specie of invaders (unwanted, flying insects), using databases of online images (for instance from the National Geographics website) - see details in presentation slides. ​
  
  
transverse.1570434104.txt.gz · Last modified: 2019/10/07 09:41 by cani