With the thriving of deep learning, CNN-based object detectors have made great progress in
the past decade. However, the domain gap between training and testing data leads to a …

In this paper, we propose a novel SpatioTemporal convolutional Dense Network (STDNet) to
address the video-based crowd counting problem, which contains the decomposition of 3D …

Deep neural networks (DNNs) have proven their capabilities in the past years and play a
significant role in environment perception for the challenging application of automated …

The development of deep learning techniques has greatly benefited CNN-based object
detectors, leading to unprecedented progress in recent years. However, the distribution …

Class-agnostic counting is usually phrased as a matching problem between a user-defined
exemplar patch and a query image. The count is derived based on the number of objects …

Hand segmentation is a crucial task in first-person vision. Since first-person images exhibit
strong bias in appearance among different environments, adapting a pre-trained …

Modifying facial attributes without the paired dataset proves to be a challenging task.
Previously, approaches either required supervision from a ground-truth transformed image …

Due to the poor illumination and the difficulty in annotating, nighttime conditions pose a
significant challenge for autonomous vehicle perception systems. Unsupervised domain …

Self-driving cars leverage on semantic segmentation to understand an urban scene.
However, it is costly to collect segmentation labels, thus, synthetic datasets are used to train …

Feature fusion is a key process of integrating multiple features in deep neural networks
(DNN). The mainstream method in the literature is based on the Feature Pyramid Network …