Attention-Based Fusion vs Concatenation in Multimodal Alignment for Zero-Shot Classification

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: Does replacing concatenation with attention-based fusion in multimodal alignment frameworks improve sample efficiency and downstream task performance on zero-shot classification benchmarks. Today, despite decades of developments in medicine and the growing interest in precision healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs of illness. Early indication and detection of diseases, however, can provide patients and carers. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

Does replacing concatenation with attention-based fusion in multimodal alignment frameworks improve sample efficiency and downstream task performance on zero-shot classification 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