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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.
Reviewers from the research community have shared their direct impressions of the work: 27cc3576a6f149e95cf68afc3e25cd6c.zip
It addresses the high query requirements of existing methods by reducing problem dimensionality and using "intrinsic-dimensional gradient clipping."
The string corresponds to a specific research paper titled "ZIP: An Efficient Zeroth-order Prompt Tuning for Black-box Vision-Language Models." It looks like there's no response available for this search
Reviewers generally agreed that the method offers superior accuracy and efficiency across multiple tasks, supported by thorough ablation studies on design choices.
While the reviews were generally positive, experts noted a few areas for improvement: Reviewers from the research community have shared their
Reviewers highlighted that the paper's design choices, specifically "feature sharing," were well-motivated and helped the model stay expressive despite the simplifications. Critical Perspectives