Motivated by recent work on deep neural network (DNN)-based image compression
methods showing potential improvements in image quality, savings in storage, and …

Continuous computer vision (CV) tasks increasingly rely on convolutional neural networks
(CNN). However, CNNs have massive compute demands that far exceed the performance …

Event-based object recognition has drawn increasing attention for event cameras'
distinguished advantages of low power consumption and high dynamic range. For this new …

Pre-trained convolutional neural networks (CNNs) are powerful off-the-shelf feature
generators and have been shown to perform very well on a variety of tasks. Unfortunately …

Hardware support for deep convolutional neural networks (CNNs) is critical to advanced
computer vision in mobile and embedded devices. Current designs, however, accelerate …

With the development of social media and the ubiquity of the Internet, recent years have
witnessed the rapid development of online video advertising among publishers and …

Abstract Deep Representation Learning technologies based on supervised Convolutional
Neural Networks (CNNs) have attained significant interest mainly due to their superior …

Human action recognition (HAR) refers to the process in which computers analyze and
process video data to obtain the categories of action presented in the video. It has a wide …

Real-time semantic video segmentation is a challenging task due to the strict requirements
of inference speed. Recent approaches mainly devote great efforts to reducing the model …

Current approaches for human pose estimation in videos can be categorized into per-frame
and warping-based methods. Both approaches have their pros and cons. For example, per …