One reviewer pointed out that the methods ZIP was compared against (like BLACKVIP and BPTVLM) were from 2023, and suggested that more recent 2024 benchmarks should have been included for a fairer comparison.
The community recognized the extensive evaluations showcasing superior accuracy and query efficiency over 13+ tasks.
It addresses the high query requirements of existing methods by reducing problem dimensionality and using "intrinsic-dimensional gradient clipping." 27cc3576a6f149e95cf68afc3e25cd6c.zip
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Reviewers pointed out that the soft prompt reparameterization design choices were thoroughly tested, including detailed ablation studies. One reviewer pointed out that the methods ZIP
This paper introduces a method called designed to improve how we tune large "black-box" models (like CLIP) when we don't have access to their internal code or gradients. Performance and Efficiency
The string corresponds to a specific research paper titled "ZIP: An Efficient Zeroth-order Prompt Tuning for Black-box Vision-Language Models." Try asking something else
Reviewers from the research community have shared their direct impressions of the work: