Knowledge Distillation Effects on Zero-Shot CLIP Retrieval Across Flickr30k and MSCOCO

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does knowledge distillation impact the zero-shot image-text retrieval accuracy of CLIP variants on Flickr30k and MSCOCO datasets. We present Distill CLIP (DCLIP), a fine-tuned variant of the CLIP model that enhances multimodal image-text retrieval while preserving the original model's strong zero-shot classification capabilities. CLIP models are typically constrained by fixed image resolutions and limited. 11 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does knowledge distillation impact the zero-shot image-text retrieval accuracy of CLIP variants on Flickr30k and MSCOCO datasets?

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