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Masking Semantic Regions for Learning from Videos
Masking out semantic regions in videos, focusing on objects and interactions rather than just backgrounds like the sky or grass, is crucial for generating more semantic representations and abstraction levels. This method involves identifying significant events and prompting the network to predict them, mirroring human visual attention that evolves over time. Understanding and leveraging this approach is vital when applying general mass modeling methods for learning from visual content.