This paper presents a survey on the latest methods of moving object detection in video
sequences captured by a moving camera. Although many researches and excellent works …

Deep learning (DL) has made breakthroughs in many computer vision tasks and also in
visual tracking. From the beginning of the research on the automatic acquisition of high …

We present an algorithm for reconstructing dense, geometrically consistent depth for all
pixels in a monocular video. We leverage a conventional structure-from-motion …

In this paper, we address the issue of data imbalance in learning deep models for visual
object tracking. Although it is well known that data distribution plays a crucial role in learning …

Recently, advances in person re-identification (Re-ID) has benefitted from use of the popular
multi-branch network. However, performing feature learning in a single branch with uniform …

The proliferation of multimedia devices over the Internet of Things (IoT) generates an
unprecedented amount of data. Consequently, the world has stepped into the era of big …

It is well known that CNNs tend to overfit to the training data. Test-time adaptation is an
extreme approach to deal with overfitting: given a test image, the aim is to adapt the trained …

Oriented object detection has gained increasing attention due to its ability to detect objects
with arbitrary orientations in the field of remote sensing (RS) images. However, the laborious …

Deep neural networks (DNNs) can resolve real-world categorization tasks with apparent
human-level performance. However, true equivalence of behavioral performance between …

Deep learning technology has enabled successful modeling of complex facial features when
high-quality images are available. Nonetheless, accurate modeling and recognition of …