Semantic segmentation is a challenging task in computer vision systems. A lot of methods
have been developed to tackle this problem ranging from autonomous vehicles, human …

The recent advances in deep neural networks have convincingly demonstrated high
capability in learning vision models on large datasets. Nevertheless, collecting expert …

Abstract Convolutional Neural Networks (CNN) have been regarded as a powerful class of
models for visual recognition problems. Nevertheless, the convolutional filters in these …

Smoke detection plays an important role in industrial safety warning systems and fire
prevention. Due to the complicated changes in the shape, texture, and color of smoke …

In this paper, we propose a Customizable Architecture Search (CAS) approach to
automatically generate a network architecture for semantic image segmentation. The …

The multimedia community has witnessed the rise of deep learning–based techniques in
analyzing multimedia content more effectively. In the past decade, the convergence of deep …

Data-driven methods with multi-sensor time series data are the most promising approaches
for monitoring machine health. Extracting fault-sensitive features from multi-sensor time …

The emergence of Neural Radiance Fields (NeRF) for novel view synthesis has increased
interest in 3D scene editing. An essential task in editing is removing objects from a scene …

Action recognition in video sequences is an interesting field for many computer vision
applications, including behavior analysis, event recognition, and video surveillance. In this …

Multi-scale learning frameworks have been regarded as a capable class of models to boost
semantic segmentation. The problem nevertheless is not trivial especially for the real-world …