Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Deep learning algorithms are a subset of the machine learning algorithms, which aim at
discovering multiple levels of distributed representations. Recently, numerous deep learning …

Clustering is a class of unsupervised learning methods that has been extensively applied
and studied in computer vision. Little work has been done to adapt it to the end-to-end …

Object detection, one of the most fundamental and challenging problems in computer vision,
seeks to locate object instances from a large number of predefined categories in natural …

Understanding and interpreting classification decisions of automated image classification
systems is of high value in many applications, as it allows to verify the reasoning of the …

Recent work has made significant progress in improving spatial resolution for pixelwise
labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous …

The latest generation of Convolutional Neural Networks (CNN) have achieved impressive
results in challenging benchmarks on image recognition and object detection, significantly …

While 3D object representations are being revived in the context of multi-view object class
detection and scene understanding, they have not yet attained wide-spread use in fine …

Convolutional neural networks (CNN) have recently shown outstanding image classification
performance in the large-scale visual recognition challenge (ILSVRC2012). The success of …

We propose bilinear models, a recognition architecture that consists of two feature extractors
whose outputs are multiplied using outer product at each location of the image and pooled …