Pith. sign in

REVIEW 1 cited by

Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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 1706.05461 v3 pith:VHL7CV3D submitted 2017-06-17 cs.CV

Zhe Wang , Kingsley Kuan , Mathieu Ravaut , Gaurav Manek , Sibo Song , Yuan Fang , Seokhwan Kim , Nancy Chen
show 8 more authors
Luis Fernando D'Haro Luu Anh Tuan Hongyuan Zhu Zeng Zeng Ngai Man Cheung Georgios Piliouras Jie Lin Vijay Chandrasekhar
This is my paper · ORCID
classification cs.CV
keywords textaudioclassificationvideocompetitionfeaturesmulti-modaltruly
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The YouTube-8M video classification challenge requires teams to classify 0.7 million videos into one or more of 4,716 classes. In this Kaggle competition, we placed in the top 3% out of 650 participants using released video and audio features. Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text. The newly introduced text data is termed as YouTube-8M-Text. We present a classification framework for the joint use of text, visual and audio features, and conduct an extensive set of experiments to quantify the benefit that this additional mode brings. The inclusion of text yields state-of-the-art results, e.g. 86.7% GAP on the YouTube-8M-Text validation dataset.

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. Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt Engineering

    cs.CV 2025-02 reject novelty 4.0 of 10

    Zero-shot GPT-4o comes close to production classifiers on several video-moderation tasks, and prompt simplification plus decomposition-aggregation improves accuracy, though key gains are partly fitted to the test set.

Pith tools