8 research outputs found
Learn More for Food Recognition via Progressive Self-Distillation
Food recognition has a wide range of applications, such as health-aware
recommendation and self-service restaurants. Most previous methods of food
recognition firstly locate informative regions in some weakly-supervised
manners and then aggregate their features. However, location errors of
informative regions limit the effectiveness of these methods to some extent.
Instead of locating multiple regions, we propose a Progressive
Self-Distillation (PSD) method, which progressively enhances the ability of
network to mine more details for food recognition. The training of PSD
simultaneously contains multiple self-distillations, in which a teacher network
and a student network share the same embedding network. Since the student
network receives a modified image from its teacher network by masking some
informative regions, the teacher network outputs stronger semantic
representations than the student network. Guided by such teacher network with
stronger semantics, the student network is encouraged to mine more useful
regions from the modified image by enhancing its own ability. The ability of
the teacher network is also enhanced with the shared embedding network. By
using progressive training, the teacher network incrementally improves its
ability to mine more discriminative regions. In inference phase, only the
teacher network is used without the help of the student network. Extensive
experiments on three datasets demonstrate the effectiveness of our proposed
method and state-of-the-art performance.Comment: Accepted by AAAI 202
A Comprehensive Review of Deep Learning-based Single Image Super-resolution
Image super-resolution (SR) is one of the vital image processing methods that
improve the resolution of an image in the field of computer vision. In the last
two decades, significant progress has been made in the field of
super-resolution, especially by utilizing deep learning methods. This survey is
an effort to provide a detailed survey of recent progress in single-image
super-resolution in the perspective of deep learning while also informing about
the initial classical methods used for image super-resolution. The survey
classifies the image SR methods into four categories, i.e., classical methods,
supervised learning-based methods, unsupervised learning-based methods, and
domain-specific SR methods. We also introduce the problem of SR to provide
intuition about image quality metrics, available reference datasets, and SR
challenges. Deep learning-based approaches of SR are evaluated using a
reference dataset. Some of the reviewed state-of-the-art image SR methods
include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN),
multiscale residual network (MSRN), meta residual dense network (Meta-RDN),
recurrent back-projection network (RBPN), second-order attention network (SAN),
SR feedback network (SRFBN) and the wavelet-based residual attention network
(WRAN). Finally, this survey is concluded with future directions and trends in
SR and open problems in SR to be addressed by the researchers.Comment: 56 Pages, 11 Figures, 5 Table