To extend the proposed STAT framework to handle class-aware annotation discrepancies across datasets, we can introduce a mechanism that focuses on learning class-specific features during the adaptation process. This can involve incorporating a class-specific attention mechanism that guides the refinement and alignment modules to pay more attention to snippets relevant to the specific action classes present in the target dataset. By emphasizing the importance of class-specific information during the refinement and alignment stages, the model can adapt more effectively to the nuances of different action categories across datasets.
Additionally, we can introduce a class-aware loss function that penalizes discrepancies in the classification and localization of specific action classes. This loss function can be designed to prioritize the accurate localization and classification of certain critical action categories that may vary significantly in their appearance or temporal characteristics across datasets. By explicitly addressing class-aware annotation differences through tailored loss functions and attention mechanisms, the STAT framework can be enhanced to handle a broader range of challenges beyond just scale variations.