The success of deep learning has been a catalyst to solving increasingly complex machine-
learning problems, which often involve multiple data modalities. We review recent advances …

The accuracies for many pattern recognition tasks have increased rapidly year by year,
achieving or even outperforming human performance. From the perspective of accuracy …

Visual place recognition has attracted widespread research interest in multiple fields such
as computer vision and robotics. Recently, researchers have employed advanced deep …

In this paper, we focus on heterogeneous feature learning for RGB-D activity recognition.
Considering that features from different channels could share some similar hidden …

Paired RGB and depth images are becoming popular multi-modal data adopted in computer
vision tasks. Traditional methods based on Convolutional Neural Networks (CNNs) typically …

Previous RGB-D fusion systems based on convolutional neural networks typically employ a
two-stream architecture, in which RGB and depth inputs are learned independently. The …

How to incorporate cross-modal complementarity sufficiently is the cornerstone question for
RGB-D salient object detection. Previous works mainly address this issue by simply …

Humans' decision making process often relies on utilizing visual information from different
views or perspectives. However, in machine-learning-based image classification we …

The use of multiple features has been shown to be an effective strategy for visual tracking
because of their complementary contributions to appearance modeling. The key problem is …

In this paper, we tackle the problem of RGB-D semantic segmentation of indoor images. We
take advantage of deconvolutional networks which can predict pixel-wise class labels, and …