mlsanityData to 7 Oct 2026

Multi-modal Ensemble Models for Predicting Video Memorability

arXiv 2102.01173 ↗First posted 1 Feb 2021cs.LG cs.AI cs.MMcited by 3

Not on the map. Why?

Abstract (arXiv)

Modeling media memorability has been a consistent challenge in the field of machine learning. The Predicting Media Memorability task in MediaEval2020 is the latest benchmark among similar challenges addressing this topic. Building upon techniques developed in previous iterations of the challenge, we developed ensemble methods with the use of extracted video, image, text, and audio features. Critically, in this work we introduce and demonstrate the efficacy and high generalizability of extracted audio embeddings as a feature for the task of predicting media memorability.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 13 references that are in the map. First posted on arXiv 1 Feb 2021.

Builds on

6 of 13 references.

References · 6

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  3. 2016CNN Architectures for Large-Scale Audio Classificationcited by 701
  4. 2018Discriminability objective for training descriptive captionscited by 67
  5. 2018VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorabilitycited by 24
  6. 2020Overview of MediaEval 2020 Predicting Media Memorability Task: What Makes a Video Memorable?cited by 11

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1.

3 papers.

  1. 2022Overview of The MediaEval 2022 Predicting Video Memorability Taskcited by 5
  2. 2024Hypergraph Multi-modal Large Language Model: Exploiting EEG and Eye-tracking Modalities to Evaluate Heterogeneous Responses for Video Understandingcited by 4
  3. 2021Predicting Media Memorability: Comparing Visual, Textual and Auditory Featurescited by 3