# Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment

arXiv: https://arxiv.org/abs/2011.02356 · HTML: https://mlsanity.com/paper/2011.02356/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [Audio-Driven Talking Face Video Generation with Dynamic Convolution Kernels](https://mlsanity.com/paper/2201.05986/) (2022)
- [VADB: A Large-Scale Video Aesthetic Database with Professional and Multi-Dimensional Annotations](https://mlsanity.com/paper/2510.25238/) (2025)
- [Pseudo-labelling and Meta Reweighting Learning for Image Aesthetic Quality Assessment](https://mlsanity.com/paper/2201.02714/) (2022)
- [Multi-Modal Aesthetic Assessment for MObile Gaming Image](https://mlsanity.com/paper/2101.11700/) (2021)
- [Considering user agreement in learning to predict the aesthetic quality](https://mlsanity.com/paper/2110.06956/) (2021)
- [Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning](https://mlsanity.com/paper/2205.03574/) (2022)
- [Aesthetic Attributes Assessment of Images with AMANv2 and DPC-CaptionsV2](https://mlsanity.com/paper/2208.04522/) (2022)
- [Peak-End-Net: A Peak-End Rule Inspired Framework for Generalizable Video Aesthetic Assessment](https://mlsanity.com/paper/2607.13941/) (2026)

## Abstract (arXiv)

Despite the growing number of unmanned aerial vehicles (UAVs) and aerial videos, there is a paucity of studies focusing on the aesthetics of aerial videos that can provide valuable information for improving the aesthetic quality of aerial photography. In this article, we present a method of deep multimodality learning for UAV video aesthetic quality assessment. More specifically, a multistream framework is designed to exploit aesthetic attributes from multiple modalities, including spatial appearance, drone camera motion, and scene structure. A novel specially designed motion stream network is proposed for this new multistream framework. We construct a dataset with 6,000 UAV video shots captured by drone cameras. Our model can judge whether a UAV video was shot by professional photographers or amateurs together with the scene type classification. The experimental results reveal that our method outperforms the video classification methods and traditional SVM-based methods for video aesthetics. In addition, we present three application examples of UAV video grading, professional segment detection and aesthetic-based UAV path planning using the proposed method.