The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

The use of RGB-D information for salient object detection (SOD) has been extensively
explored in recent years. However, relatively few efforts have been put toward modeling …

Object Detection is a task of estimating and locating an object precisely in an image. It is a
fundamental problem in computer vision and has been studied extensively in low …

Feature selection (FS) is an important component of many pattern recognition tasks. In these
tasks, one is often confronted with very high-dimensional data. FS algorithms are designed …

Most existing multiview clustering methods take graphs, which are usually predefined
independently in each view, as input to uncover data distribution. These methods ignore the …

How can we find a general way to choose the most suitable samples for training a classifier?
Even with very limited prior information? Active learning, which can be regarded as an …

Hashing or binary code learning has been recognized to accomplish efficient near neighbor
search, and has thus attracted broad interests in recent retrieval, vision, and learning …