Multimodal vs. Unimodal Models in Continual Learning: Accuracy Retention on Sequential Datasets

Abstract

Abstract: This report synthesises findings from 8 peer-reviewed papers addressing the following research question: How do multimodal models (e.g., CLIP or LXMERT) perform in continual learning scenarios compared to unimodal models, as measured by accuracy retention on sequential datasets like Visual Genome or. Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How do multimodal models (e.g., CLIP or LXMERT) perform in continual learning scenarios compared to unimodal models, as measured by accuracy retention on sequential datasets like Visual Genome or MSCOCO?

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