A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
Learning to Make Better Mistakes
2016
Proceedings of the 2016 ACM on Multimedia Conference - MM '16
We propose a visual food recognition framework that integrates the inherent semantic relationships among fine-grained classes. Our method learns semantics-aware features by formulating a multi-task loss function on top of a convolutional neural network (CNN) architecture. It then refines the CNN predictions using a random walk based smoothing procedure, which further exploits the rich semantic information. We evaluate our algorithm on a large "food-in-the-wild" benchmark [3], as well as a
doi:10.1145/2964284.2967205
dblp:conf/mm/WuMUS16
fatcat:hkruy7mbhbes7f3j527bmq7nj4