Current deep-learning models are mostly built upon neural networks, ie multiple layers of
parameterized differentiable non-linear modules that can be trained by backpropagation. In …

Accurate scale estimation of a target is a challenging research problem in visual object
tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the …

Today, artificial intelligence is everywhere in people's daily lives. Visual tracking, which is
used to identify and continuously track specific targets, is an important research domain in …

Correlation Filter-based trackers have recently achieved excellent performance, showing
great robustness to challenging situations exhibiting motion blur and illumination changes …

Robust and accurate visual tracking is one of the most challenging computer vision
problems. Due to the inherent lack of training data, a robust approach for constructing a …

Although the correlation filter-based trackers achieve the competitive results both on
accuracy and robustness, there is still a need to improve the overall tracking capability. In …

Robust scale estimation is a challenging problem in visual object tracking. Most existing
methods fail to handle large scale variations in complex image sequences. This paper …

The core component of most modern trackers is a discriminative classifier, tasked with
distinguishing between the target and the surrounding environment. To cope with natural …

Recent research explosion on Neural Radiance Field (NeRF) shows the encouraging
potential to represent complex scenes with neural networks. One major drawback of NeRF is …

Real-world tasks in computer vision often touch upon open set recognition: multi-class
recognition with incomplete knowledge of the world and many unknown inputs. Recent work …