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2021 | OriginalPaper | Buchkapitel

S2ML-TL Framework for Multi-label Food Recognition

verfasst von : Bhalaji Nagarajan, Eduardo Aguilar, Petia Radeva

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

Transfer learning can be attributed to several recent breakthroughs in deep learning. It has shown upbeat performance improvements, but most of the transfer learning applications are confined towards fine-tuning. Transfer learning facilitates the learnability of the networks on domains with less data. However, learning becomes a difficult task with complex domains, such as multi-label food recognition, owing to the shear number of food classes as well as to the fine-grained nature of food images. For this purpose, we propose S2ML-TL, a new transfer learning framework to leverage the knowledge learnt on a simpler single-label food recognition task onto multi-label food recognition. The framework is further enhanced using class priors to tackle the dataset bias that exists between single-label and multi-label food domains. We validate the proposed scheme with two multi-label datasets on different backbone architectures and the results show improved performance compared to the conventional transfer learning approach.

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Metadaten
Titel
S2ML-TL Framework for Multi-label Food Recognition
verfasst von
Bhalaji Nagarajan
Eduardo Aguilar
Petia Radeva
Copyright-Jahr
2021
DOI
https://doi.org/10.1007/978-3-030-68821-9_50