How do we learn to recognize visual categories, such as dogs and cats? Somehow, the
brain uses limited variable examples to extract the essential characteristics of new visual …

We consider the problem of predicting the 3D shape, articulation, viewpoint, texture, and
lighting of an articulated animal like a horse given a single test image as input. We present a …

During the last half decade, convolutional neural networks (CNNs) have triumphed over
semantic segmentation, which is a core task of various emerging industrial applications such …

Recognition algorithms based on convolutional networks (CNNs) typically use the output of
the last layer as feature representation. However, the information in this layer may be too …

While convolutional neural networks have gained impressive success recently in solving
structured prediction problems such as semantic segmentation, it remains a challenge to …

Decision forests (also known as random forests) are an indispensable tool for automatic
image analysis. This practical and easy-to-follow text explores the theoretical underpinnings …

In this paper we propose a novel approach for detecting interest points invariant to scale and
affine transformations. Our scale and affine invariant detectors are based on the following …

We propose a two-class classification model for grouping. Human segmented natural
images are used as positive examples. Negative examples of grouping are constructed by …

We present a method to learn and recognize object class models from unlabeled and
unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible …

We present a mobile vision system for multi-person tracking in busy environments.
Specifically, the system integrates continuous visual odometry computation with tracking-by …