Previous work generally believes that improving the spatial invariance of convolutional
networks is the key to object counting. However, after verifying several mainstream counting …

This paper investigates crowd counting in the frequency domain, which is a novel direction
compared to the traditional view in the spatial domain. By transforming the density map into …

In today's scenario, there are frequent events (viz. political rallies, live concerts, strikes,
sports meet) occur in which many people gather to participate in the event. In crowded areas …

Cameras in modern devices such as smartphones, satellites and medical equipment are
capable of capturing very high resolution images and videos. Such high-resolution data …

Recently, indiscernible scene understanding has attracted a lot of attention in the vision
community. We further advance the frontier of this field by systematically studying a new …

Crowd counting in smart surveillance systems plays a crucial role in Internet of Things (IoT)
and smart cities, and can affect various aspects, such as public safety, crowd management …

Supervised crowd counting relies on manual labeling, which is costly and time-consuming.
This led to an increased interest in unsupervised methods. However, there is a significant …

Typical approaches that learn crowd density maps are limited to extracting the supervisory
information from the loosely organized spatial information in the crowd dot/density maps …

In this article, we propose a simple yet effective approach, called point adversarial self
mining (PASM), to improve the recognition accuracy in facial expression recognition (FER) …

Domain adaptation is commonly employed in crowd counting to bridge the domain gaps
between different datasets. However, existing domain adaptation methods tend to focus on …