Spatiotemporal Representation Learning and Graph-Based Event Reasoning for Complex Video Understanding
DOI:
https://doi.org/10.66238/fsrma59Keywords:
Spatiotemporal Learning, Event Reasoning, Video Understanding, Vision TransformersAbstract
The exponential growth of digital video content necessitates advanced computational models capable of not only processing high-dimensional pixel data but also understanding intricate semantic interactions over time. Spatiotemporal representation learning has fundamentally transformed the landscape of video understanding, evolving from foundational convolutional methodologies to sophisticated attention-based architectures. However, capturing raw spatiotemporal features often falls short of achieving high-level semantic event reasoning, which requires the explicit modeling of relationships between dynamic entities across varying temporal scales. This paper presents a comprehensive investigation into the integration of spatiotemporal feature extraction with explicit graph-based event reasoning mechanisms. We propose a unified architectural framework that leverages a multi-scale vision transformer to encode dense spatiotemporal representations, seamlessly coupled with a dynamic graph convolutional network designed to reason over abstracted object and actor entities. By mapping pixel-level embeddings to semantic nodes, the system effectively models complex temporal dependencies and causal event structures. Through extensive empirical analysis on large-scale benchmarks, including Kinetics-400 and Something-Something V2, we demonstrate that incorporating explicit relational reasoning significantly improves predictive accuracy, particularly for long-horizon action recognition and temporal action localization tasks. The findings suggest a paradigm shift towards hybrid models that fuse deep representation learning with structured symbolic reasoning, offering enhanced interpretability and generalization in video understanding domains.
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