# A Late Fusion Framework with Multiple Optimization Methods for Media Interestingness

arXiv: https://arxiv.org/abs/2207.04762 · HTML: https://mlsanity.com/paper/2207.04762/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 11 Jul 2022.

## Builds on (most cited first, 4 of 4)

- [Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion](https://mlsanity.com/paper/2006.08159/) (2020)
- [Visual Sentiment Analysis from Disaster Images in Social Media](https://mlsanity.com/paper/2009.03051/) (2020)
- [An Annotated Video Dataset for Computing Video Memorability](https://mlsanity.com/paper/2112.02303/) (2021)
- [Exploring the contextual factors affecting multimodal emotion recognition in videos](https://mlsanity.com/paper/2004.13274/) (2020)

## Built on by (most cited first, 1 of 1)

- [Social Media and Artificial Intelligence for Sustainable Cities and Societies: A Water Quality Analysis Use-case](https://mlsanity.com/paper/2404.14977/) (2024)

## Abstract (arXiv)

The recent advancement in Multimedia Analytical, Computer Vision (CV), and Artificial Intelligence (AI) algorithms resulted in several interesting tools allowing an automatic analysis and retrieval of multimedia content of users' interests. However, retrieving the content of interest generally involves analysis and extraction of semantic features, such as emotions and interestingness-level. The extraction of such meaningful information is a complex task and generally, the performance of individual algorithms is very low. One way to enhance the performance of the individual algorithms is to combine the predictive capabilities of multiple algorithms using fusion schemes. This allows the individual algorithms to complement each other, leading to improved performance. This paper proposes several fusion methods for the media interestingness score prediction task introduced in CLEF Fusion 2022. The proposed methods include both a naive fusion scheme, where all the inducers are treated equally and a merit-based fusion scheme where multiple weight optimization methods are employed to assign weights to the individual inducers. In total, we used six optimization methods including a Particle Swarm Optimization (PSO), a Genetic Algorithm (GA), Nelder Mead, Trust Region Constrained (TRC), and Limited-memory Broyden Fletcher Goldfarb Shanno Algorithm (LBFGSA), and Truncated Newton Algorithm (TNA). Overall better results are obtained with PSO and TNA achieving 0.109 mean average precision at 10. The task is complex and generally, scores are low. We believe the presented analysis will provide a baseline for future research in the domain.