With the success of deep learning in the field of computer vision, object recognition has
made important breakthroughs, and its recognition accuracy has been drastically improved …

Class imbalance is a common problem in the case of real-world object detection and
classification tasks. Data of some classes are abundant, making them an overrepresented …

Evidence is mounting that ConvNets are the best representation learning method for
recognition. In the common scenario, a ConvNet is trained on a large labeled dataset and …

Evidence is mounting that Convolutional Networks (ConvNets) are the most effective
representation learning method for visual recognition tasks. In the common scenario, a …

Scene recognition plays an important role in the task of visual information retrieval,
segmentation and image/video understanding. Traditional approaches for scene recognition …

In existing convolutional neural networks (CNNs), both convolution and pooling are locally
performed for image regions separately, no contextual dependencies between different …

In this paper, we present a feature description method called semantic descriptor with
objectness (SDO) for scene recognition. Most existing scene representation methods exploit …

Convolutional neural network (CNN) has achieved the state-of-the-art performance in many
different visual tasks. Learned from a large-scale training data set, CNN features are much …

Given a single outdoor image, this paper proposes a collaborative learning approach for
labeling it as either sunny or cloudy. Never adequately addressed, this twoclass …

Dissimilar to object classification, scene classification needs to consider not only the
components that exist in the image but also their corresponding distribution. The greatest …