As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for develo** new …

Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …

The class activation maps are generated from the final convolutional layer of CNN. They can
highlight discriminative object regions for the class of interest. These discovered object …

The recent development in learning deep representations has demonstrated its wide
applications in traditional vision tasks like classification and detection. However, there has …

In this paper, we address the problem of tracking an object in a video given its location in the
first frame and no other information. Recently, a class of tracking techniques called “tracking …

We propose a method to identify and localize object classes in images. Instead of operating
at the pixel level, we advocate the use of superpixels as the basic unit of a class …

Labeling a histopathology image as having cancerous regions or not is a critical task in
cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster …

It is common to use domain specific terminology–attributes–to describe the visual
appearance of objects. In order to scale the use of these describable visual attributes to a …

Learning a new object class from cluttered training images is very challenging when the
location of object instances is unknown, ie in a weakly supervised setting. Many previous …

Learning to localize objects with minimal supervision is an important problem in computer
vision, since large fully annotated datasets are extremely costly to obtain. In this paper, we …