With the improvement of hardware computing power and the development of deep learning
algorithms, a revolution of" artificial intelligence (AI)+ medical image" is taking place …

A long time ago in the machine learning literature, the idea of incorporating a mechanism
inspired by the human visual system into neural networks was introduced. This idea is …

Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …

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

Abstract Online Multi-Object Tracking (MOT) has wide applications in time-critical video
analysis scenarios, such as robot navigation and autonomous driving. In tracking-by …

Modeling visual attention-particularly stimulus-driven, saliency-based attention-has been a
very active research area over the past 25 years. Many different models of attention are now …

We describe a model based on a Boltzmann machine with third-order connections that can
learn how to accumulate information about a shape over several fixations. The model uses a …

Object recognition systems constitute a deeply entrenched and omnipresent component of
modern intelligent systems. Research on object recognition algorithms has led to advances …

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 …

We formulate tracking as an online decision-making process, where a tracking agent must
follow an object despite ambiguous image frames and a limited computational budget …