Computer vision has been gaining interest in a wide range of research areas in recent
years, from medical to industrial robotics. The architecture, engineering and construction …

GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for
spatial analytics in Geography. Although much progress has been made in exploring the …

Establishing correspondence between images or scenes is a significant challenge in
computer vision, especially given occlusions, viewpoint changes, and varying object …

Multi-horizon forecasting often contains a complex mix of inputs–including static (ie time-
invariant) covariates, known future inputs, and other exogenous time series that are only …

Deep learning (DL) methods have achieved state-of-the-art performance in the task of single
image rain removal. Most of current DL architectures, however, are still lack of sufficient …

Long-term visual tracking has drawn increasing attention because it is much closer to
practical applications than short-term tracking. Most top-ranked long-term trackers adopt the …

Template-based discriminative trackers are currently the dominant tracking paradigm due to
their robustness, but are restricted to bounding box tracking and a limited range of …

Recently, deep learning has achieved great success in visual tracking. The goal of this
paper is to review the state-of-the-art tracking methods based on deep learning. First, we …

Hyperspectral video offers a wealth of material and motion cues about objects. This
advantage proves invaluable in addressing the inherent limitations of generic RGB video …

While color information is known to provide rich discriminative clues for visual inference,
most modern visual trackers limit themselves to the grayscale realm. Despite recent efforts to …