Holistic Video Understanding


Holistic Video Understanding Dataset

Holistic Large Scale Video Understanding


Action recognition has been advanced in recent years by benchmarks with rich annotations. However, research is still mainly limited to human action or sports recognition - focusing on a highly specific video understanding task and thus leaving a significant gap towards describing the overall content of a video. We fill in this gap by presenting a large-scale "Holistic Video Understanding Dataset"~(HVU). HVU is organized hierarchically in a semantic taxonomy that focuses on multi-label and multi-task video understanding as a comprehensive problem that encompasses the recognition of multiple semantic aspects in the dynamic scene. HVU contains approx.~577k videos in total with 13M annotations for training and validation set spanning over 4378 classes. HVU encompasses semantic aspects defined on categories of scenes, objects, actions, events, attributes and concepts, which naturally captures the real-world scenarios.

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* Ali Diba, Mohsen Fayyaz, and Vivek Sharma contributed equally to this work and listed in alphabetical order.