# Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network

arXiv: https://arxiv.org/abs/1906.00377 · HTML: https://mlsanity.com/paper/1906.00377/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [A Straightforward Framework For Video Retrieval Using CLIP](https://mlsanity.com/paper/2102.12443/) (2021)
- [Reconstructive Sequence-Graph Network for Video Summarization](https://mlsanity.com/paper/2105.04066/) (2021)
- [SeiT: Storage-Efficient Vision Training with Tokens Using 1% of Pixel Storage](https://mlsanity.com/paper/2303.11114/) (2023)
- [Enhancing Transformer for Video Understanding Using Gated Multi-Level Attention and Temporal Adversarial Training](https://mlsanity.com/paper/2103.10043/) (2021)
- [Influential Rank: A New Perspective of Post-training for Robust Model against Noisy Labels](https://mlsanity.com/paper/2106.07217/) (2021)
- [Semi-Supervised Audio-Visual Video Action Recognition with Audio Source Localization Guided Mixup](https://mlsanity.com/paper/2503.02284/) (2025)

## Abstract (arXiv)

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.