Guest Editorial Introduction to the Special Issue on Advanced Machine Learning Methodologies for Large-Scale Video Object Segmentation and Detection

Abstract

Video object segmentation and detection are two important tasks toward intelligent video content understanding. Due to their wide applications in real-world vision tasks, such as video surveillance and automatic driving, they have recently attracted great attention in the computer vision and multimedia processing communities. Although numerous deep learning-based approaches have been proposed to solve these problems, implementing effective and efficient video object segmentation and detection is still very challenging for now, and the principles of solutions to address the problems are still understudied. On the one hand, the features learned by the current deep models are not strong enough to capture the rich spatial and temporal information from the input videos. On the other hand, the annotation information in the video data (especially for unconstrained online videos) is usually insufficient, unspecific, or even absent, thus challenging the current mainstream learning schemes.