A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action Recognition

Understand

The goal of building a benchmark (suite of datasets) is to provide a unified protocol for fair evaluation and thus facilitate the evolution of a specific area.

• Nonetheless, we point out that existing protocols of action recognition could yield partial evaluations due to several limitations.
• To comprehensively probe the effectiveness of spatiotemporal representation learning, we introduce BEAR, a new BEnchmark on video Action Recognition.
• BEAR is a collection of 18 video datasets grouped into 5 categories (anomaly, gesture, daily, sports, and instructional), which covers a diverse set of real-world applications.