# Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network

## TL;DR

This paper introduces a deep convolutional graph neural network that captures hierarchical relationships in video frame sequences, improving classification accuracy over RNN-based methods on large-scale datasets.

## Contribution

The paper proposes a novel hierarchical video representation method using a deep convolutional graph network, addressing limitations of RNNs on long video sequences.

## Key findings

- Outperforms RNN benchmarks on YouTube-8M dataset
- Effectively captures hierarchical event semantics
- Enhances video classification accuracy

## Abstract

High accuracy video label prediction (classification) models are attributed to large scale data. These data could be frame feature sequences extracted by a pre-trained convolutional-neural-network, which promote the efficiency for creating models. Unsupervised solutions such as feature average pooling, as a simple label-independent parameter-free based method, has limited ability to represent the video. While the supervised methods, like RNN, can greatly improve the recognition accuracy. However, the video length is usually long, and there are hierarchical relationships between frames across events in the video, the performance of RNN based models are decreased. In this paper, we proposes a novel video classification method based on a deep convolutional graph neural network(DCGN). The proposed method utilize the characteristics of the hierarchical structure of the video, and performed multi-level feature extraction on the video frame sequence through the graph network, obtained a video representation re ecting the event semantics hierarchically. We test our model on YouTube-8M Large-Scale Video Understanding dataset, and the result outperforms RNN based benchmarks.