Recent progress in semantic segmentation has been driven by improving the spatial
resolution under Fully Convolutional Networks (FCNs). To address this problem, we propose …

Abstract Convolutional Neural Network based action recognition methods have achieved
significant improvements in recent years. The 3D convolution extends the 2D convolution to …

Monocular depth estimation is very challenging in complex compositions depicting multiple
objects of diverse scales. Albeit the recent great progress thanks to the deep convolutional …

Fatigue detection for drivers in public transportation is crucial. To effectively detect the
driver's fatigue state, we investigated the deep learning-based fatigue detection method and …

In this paper, color-depth conditional generative adversarial networks (CDcGAN) are
proposed to resolve the problems of simultaneous color image super-resolution and depth …

Existing monocular depth estimation methods are unsatisfactory due to the inaccurate
inference of depth details and the loss of spatial information. In this paper, we present a …

Abstract Domain adaptation is a significant and popular issue of solving distribution
discrepancy among different domains in computer vision. Generally, previous works …

In this paper, we propose a Contextual Deconvolution Network (CDN) and focus on context
association in decoder network. Specifically, in upsampling path, we introduce two types of …

The task of the proposed method is semi-supervised video object segmentation where only
the ground-truth segmentation of the first frame is provided. The existing approaches rely on …

The analysis of the video shot in basketball games and the edge detection of the video shot
are the most active and rapid development topics in the field of multimedia research in the …