Learning from demonstration, or imitation learning, is the process of learning to act in an
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …

Applying convolutional neural networks to large images is computationally expensive
because the amount of computation scales linearly with the number of image pixels. We …

Manually annotating object bounding boxes is central to building computer vision datasets,
and it is very time consuming (annotating ILSVRC [53] took 35s for one high-quality box …

Traditional eye tracking requires specialized hardware, which means collecting gaze data
from many observers is expensive, tedious and slow. Therefore, existing saliency prediction …

Research in visual saliency has been focused on two major types of models namely fixation
prediction and salient object detection. The relationship between the two, however, has …

Top-down visual saliency is an important module of visual attention. In this work, we propose
a novel top-down saliency model that jointly learns a Conditional Random Field (CRF) and a …

One of the most widely used strategies for visual object detection is based on exhaustive
spatial hypothesis search. While methods like sliding windows have been successful and …

Most saliency estimation methods aim to explicitly model low-level conspicuity cues such as
edges or blobs and may additionally incorporate top-down cues using face or text detection …

Systems based on bag-of-words models from image features collected at maxima of sparse
interest point operators have been used successfully for both computer visual object and …

In many computer vision tasks, the relevant information to solve the problem at hand is
mixed to irrelevant, distracting information. This has motivated researchers to design …