Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Large-scale annotated datasets are scarce for video activity understanding.
• Transfer learning is crucial for developing effective action classification models with limited data.

Purpose of the Study:

• To develop an effective few-shot transfer learning method for first-person action classification.
• To enable robust action classification across diverse datasets with minimal labeled examples.

Main Methods:

• Leveraging independently trained local visual cues (object interactions, hand grasps, motion).
• Employing a meta-learning framework to extract domain-invariant representations.
• Transferring learned representations from source to target domains with few examples.

Main Results:

• Achieved superior performance in action classification compared to state-of-the-art methods.
• Demonstrated effectiveness in both inter-class and inter-dataset transfer scenarios.
• Successfully transferred models across diverse public datasets with varying configurations.

Conclusions:

• The proposed few-shot transfer learning methodology is highly effective for video activity understanding.
• Meta-learning enhances the transferability of visual cues for action classification.
• This approach addresses the challenge of data scarcity in video analysis.