Robustness Metrics for Indonesian Video-Text Models: PaLI vs. Flamingo on MSRVTT

Abstract

Abstract: This report synthesises findings from 8 peer-reviewed papers addressing the following research question: What metrics (e.g., BLEU, CIDEr, METEOR) demonstrate the robustness of Indonesian video-text models like MSVD-Indonesian when fine-tuned with PaLI versus Flamingo on MSRVTT, and how does this compare. While vision-language pre-trained models (VL-PTMs) have advanced multimodal research in recent years, their mastery in a few languages like English restricts their applicability in broader communities. To this end, there is an increasing interest in developing multilingual VL. 17 claims were extracted from source literature; 13 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What metrics (e.g., BLEU, CIDEr, METEOR) demonstrate the robustness of Indonesian video-text models like MSVD-Indonesian when fine-tuned with PaLI versus Flamingo on MSRVTT, and how does this compare to English benchmarks?

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