# A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset

**Authors:** Jiaxin Deng, Dong Shen, Haojie Pan, Xiangyu Wu, Ximan Liu, Gaofeng Meng, Fan Yang, Size Li, Ruiji Fu, Zhongyuan Wang  
**arXiv:** [2211.10624](https://arxiv.org/abs/2211.10624)  
**Date:** 2022-11-19  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2211.10624v2/

## Key points

- Authors emphasize the importance of integrating common sense knowledge from external Knowledge Graph datasets to enhance video understanding.
- Lack of a comprehensive video knowledge graph dataset has limited previous works combining video understanding with KG.
- Authors propose a heterogeneous dataset including multi-modal video entities and rich common sense relations to address this gap.
- Introduction of novel video inference tasks such as Video-Relation-Tag (VRT) and Video-Relation-Video (VRV) tasks using the proposed dataset.
- Presentation of an end-to-end model optimizing both video understanding objective and knowledge graph embedding, enhancing factual knowledge integration into video understanding.
- Comprehensive experiments demonstrate benefits of combining video understanding embedding with factual knowledge, showing improved content-based video retrieval performance and better knowledge graph embeddings compared to traditional methods.
- Model outperforms existing approaches on VRT and VRV tasks, achieving significant improvements in HITS@10 metrics - at least 42.36% and 17.73% enhancements respectively.
- Research highlights potential of leveraging heterogeneous knowledge graphs to enhance video understanding capabilities for content-based recommendation and classification in short video platforms.

## Summary

In their paper titled "A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset," authors Jiaxin Deng, Dong Shen, Haojie Pan, Xiangyu Wu, Ximan Liu, Gaofeng Meng, Fan Yang, Size Li, Ruiji Fu, and Zhongyuan Wang address the significance of video understanding in short video business platforms. They emphasize the importance of integrating common sense knowledge from external Knowledge Graph (KG) datasets to enhance video understanding beyond just the content within the video frames, audio, and text. The lack of a comprehensive video knowledge graph dataset has limited previous works that aim to combine video understanding with KG. To bridge this gap, the authors propose a heterogeneous dataset that includes multi-modal video entities and rich common sense relations. This dataset introduces novel video inference tasks such as Video-Relation-Tag (VRT) and Video-Relation-Video (VRV) tasks. Building on this dataset, they present an end-to-end model that optimizes both the video understanding objective and knowledge graph embedding. This approach not only enhances factual knowledge integration into video understanding but also generates effective multi-modal entity embeddings for KG. The authors conduct comprehensive experiments to demonstrate the benefits of combining video understanding embedding with factual knowledge. Their results show improved content-based video retrieval performance and better knowledge graph embeddings compared to traditional KG-based methods. Specifically, their model outperforms existing approaches on VRT and VRV tasks with significant improvements in HITS@10 metrics - achieving at least 42.36% and 17.73% enhancements respectively. Overall, this research highlights the potential of leveraging heterogeneous knowledge graphs to enhance video understanding capabilities and improve overall performance in tasks related to content-based recommendation and classification in short video platforms.