Non-Autoregressive vs. Autoregressive Multimodal Models: Inference Speed on MSR-VTT and MSVD

Abstract

Abstract: This report synthesises findings from 6 peer-reviewed papers addressing the following research question: How does the inference speed of non-autoregressive multimodal models compare to autoregressive models on the MSR-VTT and MSVD benchmarks when evaluated using frames-per-second (FPS) or latency metrics. It is encouraged to see that progress has been made to bridge videos and natural language. However, mainstream video captioning methods suffer from slow inference speed due to the sequential manner of autoregressive decoding, and prefer generating generic descriptions due to the. 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

How does the inference speed of non-autoregressive multimodal models compare to autoregressive models on the MSR-VTT and MSVD benchmarks when evaluated using frames-per-second (FPS) or latency metrics?

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