Global covariance pooling in convolutional neural networks has achieved impressive
improvement over the classical first-order pooling. Recent works have shown matrix square …

Compared with global average pooling in existing deep convolutional neural networks
(CNNs), global covariance pooling can capture richer statistics of deep features, having …

Exploiting the spatial structure in scene images is a key research direction for scene
recognition. Due to the large intra-class structural diversity, building and modeling flexible …

Scene classification, aiming at classifying a scene image to one of the predefined scene
categories by comprehending the entire image, is a longstanding, fundamental and …

Many different deep networks have been used to approximate, accelerate or improve
traditional image operators, such as image smoothing, super-resolution and denoising …

Scene recognition remains one of the most challenging problems in image understanding.
With the help of fully connected layers (FCL) and rectified linear units (ReLu), deep networks …

Scene classification based on convolutional neural networks (CNNs) has achieved great
success in recent years. In CNNs, the convolution operation performs well in extracting local …

What scene elements, if any, are indispensable for recognizing a scene? We strive to
answer this question through the lens of an end-to-end learning scheme. Our goal is to …

Multi-label image classification is a fundamental and challenging task in computer vision,
and recently achieved significant progress by exploiting semantic relations among labels …

RGB-D indoor scene classification is an essential and challenging task. Although
convolutional neural network (CNN) achieves excellent results on RGB-D object recognition …