Few-shot learning for fine-grained image classification has gained recent attention in
computer vision. Among the approaches for few-shot learning, due to the simplicity and …

In this paper, we propose a novel convolutional neural network (CNN) architecture, MFRNet,
for post-processing (PP) and in-loop filtering (ILF) in the context of video compression. This …

Recently, many deep learning based researches are conducted to explore the potential
quality improvement of compressed videos. These methods mostly utilize either the spatial …

The increase of video content and video resolution drive more exploration of video
compression techniques recently. Meanwhile, learning-based video compression is …

Recently, many researches on convolution neural network (CNN) based in-loop filters have
been proposed to improve coding efficiency. However, most existing CNN based filters tend …

The purpose of few-shot learning is to learn a classifier, even if only a limited number of
samples are used, a good generalization effect can be achieved. Recently, many methods …

The past few years have witnessed the great success of multi-frame quality enhancement for
compressed video. Although the existing methods based on deformable alignment have …

The amount of multimedia data, such as images and videos, has been increasing rapidly
with the development of various imaging devices and the Internet, bringing more stress and …

High efficiency video coding (HEVC) is the latest high-performance video coding standard,
and HEVC video steganography has become a new way to hide data for covert …

The deep learning-based in-loop filtering methods have greatly improved the coding
efficiency for High Efficiency Video Coding (HEVC). However, directly applying these HEVC …