Coarse-to-Fine Captioning Performance Gains in Non-Autoregressive Video Captioning Models

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: To what extent does the coarse-to-fine captioning procedure in non-autoregressive models improve CIDEr or BLEU scores on out-of-domain video captioning benchmarks like MSR-VTT compared to in-domain. Current video captioning methods usually use an encoder-decoder structure to generate text autoregressively. However, autoregressive methods have inherent limitations such as slow generation speed and large cumulative error. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

To what extent does the coarse-to-fine captioning procedure in non-autoregressive models improve CIDEr or BLEU scores on out-of-domain video captioning benchmarks like MSR-VTT compared to in-domain benchmarks like MSVD?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance