Long Term Video Understanding

Long-term video understanding aims to analyze and interpret video content spanning extended durations, enabling tasks like complex action recognition and video question answering. Current research focuses on developing efficient models that overcome the computational challenges of processing long videos, employing techniques like incremental singular value decomposition, memory-augmented architectures, and state-space models to handle both short-term actions and long-range dependencies. These advancements are crucial for improving applications ranging from video surveillance and sports analysis to more sophisticated human-computer interaction and multimodal understanding. A key challenge remains the development of truly representative datasets that necessitate genuine long-term reasoning, rather than relying on short-term cues for accurate predictions.