To further enhance the performance of the Open-vocab TAD task, the following modalities and contextual information could be incorporated:
Audio Information: Integrating audio features can provide valuable cues for action recognition, especially in scenarios where visual information is limited or ambiguous. Audio-visual fusion can improve the model's understanding of actions and enhance its performance.
Spatial Context: Incorporating spatial context information, such as object relationships and scene layout, can help the model better understand the context in which actions occur. Spatial reasoning modules can assist in inferring actions based on the spatial arrangement of objects in the scene.
Temporal Context: Considering the temporal context of actions by analyzing the sequence of actions and their dependencies can improve the model's ability to predict actions accurately over time. Temporal reasoning mechanisms like recurrent neural networks or transformers can capture long-range dependencies in action sequences.
Object Detection: Utilizing object detection information alongside action recognition can provide additional context for understanding actions in relation to objects present in the scene. By incorporating object detection features, the model can improve its action localization and identification capabilities.
Scene Understanding: Integrating scene understanding techniques that analyze the overall context of the scene, including scene semantics and dynamics, can aid in disambiguating actions and improving the model's performance in complex environments. Semantic segmentation and scene parsing can provide valuable contextual information for action recognition.