Fusion Learning using Semantics and Graph Convolutional Network for Visual Food Recognition
- Focus on food recognition in the domain of fusion learning, which combines few-shot and many-shot learning.
- This project explores using semantic information, such as class label textual embedding or hierarchical embedding to improve the image classification performance.
- Inter-class correlations in terms of image feature and text representation are explored using a designed graph convolutional network.
- The proposed method achieves state-of-the-art performance on major food benchmark datasets.
Published in WACV2021.
Overview of the Proposed Fusion Learning Framework for Food Recognition
![Overview of Proposed Fusion Learning Framework for Food Recognition](/images/librariesprovider25/default-album/overview-of-proposed-fusion-learning-framework-for-food-recognitioneb4d7978-2f71-4af5-a76e-05a1e2c942a1.jpg?Status=Master&sfvrsn=15560b05_3)
For more information, you may contact our professor Yap Kim Hui.