DZGCoOp paper: CVPR author-kit scaffold and full draft

Paper draft for the second work (thesis Ch.4 ZGDPE, renamed DZGCoOp):
abstract, intro, related work, method (zero-shot guidance module,
asymmetric dual-branch with deep prompts, confidence-guided dynamic
fusion, guidance losses + EWA), experiments (base-to-novel,
cross-domain, cross-dataset, ablations) and conclusion.

Compiles to 8 pages with tectonic (no errors, no overfull boxes).

Co-Authored-By: Claude Code <noreply@anthropic.com>

sec/5_conclusion.tex

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+\section{Conclusion}
+\label{sec:conclusion}
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+We presented DZGCoOp, a zero-shot guided dual-branch prompt learning framework for few-shot image classification. DZGCoOp distills LLM-enriched semantic anchors, zero-shot visual features, probability distributions, and an instance-level confidence from the frozen CLIP; learns an asymmetric strong-weak dual-branch network coupled through shared deep prompt layers; fuses the branches per instance with the zero-shot confidence; and regularizes optimization with feature- and distribution-level guidance losses plus an epoch-weighted averaging strategy. Experiments on base-to-novel, cross-domain, and cross-dataset transfer demonstrate consistent improvements over strong prompt learning baselines, and ablations quantify the contribution of each component. We believe that exploiting a frozen VLM's own predictive confidence to route between conservative and exploratory adaptation paths is a general recipe for knowledge-preserving few-shot learning, and we leave its extension to denser vision-language tasks as future work.
+
+\noindent\textbf{Acknowledgments.} This work was supported by the National Natural Science Foundation of China (No. 62306320) and the Natural Science Foundation of Jiangsu Province (No. BK20231063).