5 accepted papers
Lossy image compression is often limited by the simplicity of the chosen loss measure. Recent research suggests that generative adversarial networks have the ability to overcome this limitation and serve as a multi-modal loss, especially for textures. Together with learned image compression, these t…
Deep learning for image and video compression has demonstrated promising results both as a standalone technology and a hybrid combination with existing codecs. However, these systems still come with high computational costs. Deep learning models are typically applied directly in pixel space, making…
Scene Parsing is a crucial step to enable autonomous systems to understand and interact with their surroundings. Supervised deep learning methods have made great progress in solving scene parsing problems, however, come at the cost of laborious manual pixel-level annotation. Synthetic data as well a…
This paper introduces architecture with high throughput, low on-chip memory, and efficient data access for Improved Dense Trajectories (iDT) as video representations for realtime action recognition. The iDT feature can capture longterm motion cues better than any existing deep feature, which makes i…
Digital refocusing for sparsely sampled light fields results in aliasing effect. For realistic quality, previous works performed anti-aliasing by applying time-consuming view interpolation for a heuristic number of novel views. In this paper, we study this problem by first performing a spectral anal…