# Large Scale Holistic Video Understanding

## TL;DR

This paper introduces HVU, a large-scale dataset for holistic video understanding, and proposes HATNet, a neural network architecture that fuses appearance and temporal features for multi-label, multi-task video analysis.

## Contribution

The paper presents HVU, a comprehensive dataset with 572k videos and 9 million annotations across multiple semantic categories, and introduces HATNet, a novel neural network architecture for holistic video understanding.

## Key findings

- HVU enables multi-task, multi-label video analysis across diverse semantic categories.
- HATNet effectively fuses 2D and 3D features for improved video classification.
- Holistic representations improve performance in video classification, captioning, and clustering.

## Abstract

Video 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 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.~572k videos in total with 9 million annotations for training, validation, and test set spanning over 3142 labels. HVU encompasses semantic aspects defined on categories of scenes, objects, actions, events, attributes, and concepts which naturally captures the real-world scenarios. We demonstrate the generalization capability of HVU on three challenging tasks: 1.) Video classification, 2.) Video captioning and 3.) Video clustering tasks. In particular for video classification, we introduce a new spatio-temporal deep neural network architecture called "Holistic Appearance and Temporal Network"~(HATNet) that builds on fusing 2D and 3D architectures into one by combining intermediate representations of appearance and temporal cues. HATNet focuses on the multi-label and multi-task learning problem and is trained in an end-to-end manner. Via our experiments, we validate the idea that holistic representation learning is complementary, and can play a key role in enabling many real-world applications.