Inference Efficiency and Human Attention Alignment in Large-Scale Vision Models for Object Detection

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: What is the impact of model inference efficiency on the correlation between human attention prediction accuracy and downstream task performance in large-scale vision models. Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people's life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class.. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of model inference efficiency on the correlation between human attention prediction accuracy and downstream task performance in large-scale vision models?

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