Visual target tracking is one of the most sought-after yet challenging research topics in
computer vision. Given the ill-posed nature of the problem and its popularity in a broad …

Deep spectral modelling for regression and classification is gaining popularity in the
chemometrics domain. A major topic in the deep learning (DL) modelling of spectral data is …

Abstract We present Siam R-CNN, a Siamese re-detection architecture which unleashes the
full power of two-stage object detection approaches for visual object tracking. We combine …

Object tracking is still a critical and challenging problem with many applications in computer
vision. For this challenge, more and more researchers pay attention to applying deep …

We propose an unsupervised visual tracking method in this paper. Different from existing
approaches using extensive annotated data for supervised learning, our CNN model is …

In recent years, deep learning has revolutionized computer vision and has been widely used
for monitoring in diverse visual scenes. However, in terms of some aspects such as …

We propose a new Group Feature Selection method for Discriminative Correlation Filters
(GFS-DCF) based visual object tracking. The key innovation of the proposed method is to …

Hyperparameters are numerical pre-sets whose values are assigned prior to the
commencement of a learning process. Selecting appropriate hyperparameters is often …

Knowledge-transfer (KT) methods allow for transferring the knowledge contained in a large
deep learning model into a more lightweight and faster model. However, the vast majority of …

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 …