JPEG-domain enhancement improves the visual quality of JPEG images by directly manipulating the decoded DCT (discrete cosine transform) coefficients, which inevitably leads to mixed compression and enhancement artifacts. Existing forensic methods that …
Depth image-based rendering (DIBR) techniques play an important role in free viewpoint videos (FVVs), which have a wide range of applications including immersive entertainment, remote monitoring, education, etc. FVVs are usually synthesized by DIBR tec…
Under ideal environmental conditions, RGB-based deep convolutional neural networks can achieve high performance for salient object detection (SOD). In scenes with cluttered backgrounds and many objects, depth maps have been combined with RGB images to …
Presents the table of contents for this issue of the publication.
Facial expression recognition (FER) is a challenging task due to the large appearance variations and the lack of sufficient training data. Conventional deep approaches either learn a good representation through deep models or synthesize images automati…
Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data. Previous works focus on improving the domain adaptability of region-b…
View-synthesis-based methods have shown very promising results for the task of unsupervised depth estimation in single images. Most existing approaches synthesize a new image and employ it as the supervision signal for depth and pose prediction. There …
In recent years, convolutional regression trackers have shown increasing attention for visual object tracking due to their favorable performance and easy implementation. However, most of them are restricted to features from a certain layer and hardly b…
The proliferation of low-cost DRAM chipsets now begins to allow for the consideration of substantially-increased decoded picture buffers in advanced video coding standards such as HEVC, VVC, and Google VP9. At the same time, the increasing demand for r…
Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and contribute remarkable progress. However, most of the existing CNNs-based SISR methods do not adequately explore contextual informa…