AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Across visual recognition, vision-language understanding, ego-exo video understanding, and embodied vision-language-action learning, this method consistently improves learning under online and uncertain data streams, with gains exceeding 50 percentage points over replay-free alternatives in embodied manipulation.
This work introduces Continual Ego, Exo, and Ego-Exo Learning (CE$^4$L), a unified multi-view CL benchmark spanning four representative tasks: cross-view referenced skill assessment, temporal action segmentation, cross-view association, and action anticipation&planning.
Anchored by this tri-axial framework, representative methods are systematically surveyed, the ongoing transition of continual learning is traced, and the key challenges, broader implications, and future directions arising from this paradigm shift are discussed.
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