Pith. sign in

REVIEW 1 cited by

Leveraging Audio Gestalt to Predict Media Memorability

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2012.15635 v1 pith:SLSKQMHR submitted 2020-12-31 cs.MM cs.AIcs.CV

classification cs.MMcs.AIcs.CV
keywords memorabilitymediaaudiovideofeaturesgestaltpredictpredicting
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Memorability determines what evanesces into emptiness, and what worms its way into the deepest furrows of our minds. It is the key to curating more meaningful media content as we wade through daily digital torrents. The Predicting Media Memorability task in MediaEval 2020 aims to address the question of media memorability by setting the task of automatically predicting video memorability. Our approach is a multimodal deep learning-based late fusion that combines visual, semantic, and auditory features. We used audio gestalt to estimate the influence of the audio modality on overall video memorability, and accordingly inform which combination of features would best predict a given video's memorability scores.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Generative Outpainting To Enhance the Memorability of Short-Form Videos

    cs.CV 2024-11 reject novelty 5.0 of 10

    Applying M3DDM and MOTIA outpainting to short videos shifts AI-predicted memorability scores, generally helping low-memorability videos and hurting high-memorability ones, but the effects are small and not statistical...

Pith tools