LogFA: Efficient Feature-Space Data Augmentation for Egocentric Temporal Action Segmentation

Egocentric AI assistants seek to infer user actions and intentions from long first-person videos to facilitate real-world tasks ranging from daily activities to industrial operations. However, collecting large-scale annotated egocentric videos that capture variations in objects, tools, environments and procedures is costly and impractical, motivating the need for data-efficient learning. Existing data augmentation approaches either offer limited visual diversity through simple transformations or rely on computationally expensive generative models that may introduce artifacts. We propose LogFA (Local-global Featurespace Augmentation), a novel framework that performs data augmentation directly in the feature space for Temporal Action Segmentation (TAS). LogFA efficiently generates semantically consistent variations without pixel-level synthesis by modifying pre-extracted video features at both local and global levels. Locally, LogFA leverages visionlanguage models with a Prompt-based Feature Enhancement strategy to create diverse action-level variations through text-guided feature modifications. Globally, LogFA constructs a Generalized Directed Acyclic Graph to model procedural dependencies and sample alternative action sequences. Experiments on egocentric video benchmarks show LogFA significantly improves model generalization to unseen environments while maintaining low computational and data collection costs. Code will be released at https://github.com/ZijiaLewisLu/ECCV2026- LogFA.