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Recognition and Localization of Food in Cooking Videos

Nachwa Aboubakr 1 Rémi Ronfard 2 James L. Crowley 1
1 PERVASIVE - Interaction située avec les objets et environnements intelligents
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
2 IMAGINE - Intuitive Modeling and Animation for Interactive Graphics & Narrative Environments
Inria Grenoble - Rhône-Alpes, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann
Abstract : In this paper, we describe experiments with techniques for locating foods and recognizing food states in cooking videos. We describe production of a new data set that provides annotated images for food types and food states. We compare results with two techniques for detecting food types and food states, and then show that recognizing type and state with separate classifiers improves recognition results. We then use this to provide detection of composite activation maps for food types. The results provide a promising first step towards construction of narratives for cooking actions.
Keywords : Object localization Weakly supervised learning
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01815512
Contributor : Nachwa Aboubakr <>
Submitted on : Friday, July 6, 2018 - 7:54:43 PM
Last modification on : Thursday, November 19, 2020 - 1:02:25 PM

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INRIA | UGA | LJK_GI | LJK_GI_IMAGINE | LJK | LIG | CNRS | LIG_SIC_PERVASIVE

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Nachwa Aboubakr, Rémi Ronfard, James L. Crowley. Recognition and Localization of Food in Cooking Videos. Joint Workshop on Multimedia for Cooking and Eating Activities and Multimedia Assisted Dietary Management (CEA-MADiMa 2018), Jul 2018, Stockholm, Sweden. pp.21-24, ⟨10.1145/3230519.3230590⟩. ⟨hal-01815512v3⟩

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