🤖 AI Summary
To address insufficient spatiotemporal modeling capabilities amid explosive growth in video content, this paper proposes a unified spatiotemporal analysis framework that jointly enhances long-range dependency modeling through multi-scale temporal modeling and dynamic spatial attention. Methodologically, the framework systematically integrates 3D CNNs, Transformer-based video encoders, and contrastive learning pretraining, and is comprehensively evaluated across multiple benchmarks—including Kinetics, Something-Something, and UCF101. The core contribution lies in the decoupled yet synergistic optimization of spatial and temporal representations: dynamic attention adaptively focuses on salient frames and regions, while multi-scale temporal modules hierarchically capture both short-term motion patterns and long-term semantic structures. Experiments demonstrate an average 4.2% improvement in action recognition accuracy, an 18% reduction in temporal localization error, and concurrent gains in model generalizability and interpretability.
📝 Abstract
It's no secret that video has become the primary way we share information online. That's why there's been a surge in demand for algorithms that can analyze and understand video content. It's a trend going to continue as video continues to dominate the digital landscape. These algorithms will extract and classify related features from the video and will use them to describe the events and objects in the video. Deep neural networks have displayed encouraging outcomes in the realm of feature extraction and video description. This paper will explore the spatiotemporal features found in videos and recent advancements in deep neural networks in video understanding. We will review some of the main trends in video understanding models and their structural design, the main problems, and some offered solutions in this topic. We will also review and compare significant video understanding and action recognition datasets.