Video2vec Embeddings Recognize Events When Examples Are Scarce.

Area of Science:

• Computer Vision
• Machine Learning
• Multimedia Analysis

Background:

• Event recognition in videos is challenging, especially with limited or absent training examples.
• Effective semantic video representation is crucial for overcoming data scarcity in event recognition tasks.
• Existing methods often rely on attribute detectors, which may not generalize well to unseen events.

Purpose of the Study:

• To develop a robust semantic video representation for event recognition in low-data regimes.
• To leverage freely available web videos and their descriptions for learning video representations.
• To improve the accuracy of few- and zero-example event recognition.

Main Methods:

• Proposed Video2vec, an embedding that learns from video features and text descriptions using joint optimization.
• Utilized multimodal predictability loss (appearance, motion, audio) for a more predictable representation.
• Introduced an event-specific variant with term-sensitive descriptiveness loss for enhanced word representation.

Main Results:

• Video2vec outperformed attribute-based representations and alternative embeddings on event recognition tasks.
• Fusing video modalities through embedding improved performance compared to common fusion strategies.
• Demonstrated the complementary benefits of term-sensitive descriptiveness and multimodal predictability.

Conclusions:

• Video2vec achieves state-of-the-art accuracy in few- and zero-example event recognition.
• The approach effectively balances feature predictability with semantic descriptiveness.
• This method offers a significant advancement for recognizing events in videos with scarce data.