Self-Supervised and Multimodal Learning for Intelligent Video Analysis

Self-supervised and multimodal learning techniques have emerged as powerful approaches for enhancing intelligent video analysis by reducing dependence on large labeled datasets and integrating diverse data sources. Self-supervised learning enables models to learn meaningful visual representations from unlabeled video data, while multimodal learning combines information from multiple sources such as video, audio, text, and sensor data to improve understanding and decision-making. These approaches support advanced applications including action recognition, event detection, surveillance, and human behavior analysis. This study explores recent developments, challenges, and future directions in building accurate, scalable, and adaptive video intelligence systems using self-supervised and multimodal learning frameworks.

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