BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning
Mar 26, 2023 · In this work, we propose black-box visual prompting (BlackVIP), which efficiently adapts the PTMs without knowledge about model architectures and parameters. BlackVIP has two components; 1) Coordinator and 2) simultaneous perturbation stochastic approximation with gradient correction (SPSA-GC).
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In this work, we propose black-box visual prompting (Black-VIP), which eficiently adapts the PTMs without knowl-edge about model architectures and parameters. Black-VIP has two components; 1) Coordinator and 2) simul-taneous perturbation stochastic approximation with gradi-ent correction (SPSA-GC).
BlackVIP in a nutshell. Learns visual prompts (perturbations) to steer the model to produce desirable output. Prompts are obtained by training a prompt generator via zeroth-order optimization. After training, BlackVIP automatically generates an input-dependent visual prompt.
VP learns a universal visual prompt per each downstream task, and it just adds to all of the images in a task. Unlike the AR methods or our BlackVIP, the range of prompted images is unbounded. Following [2], we use the padding-style prompt, …
BlackVIP/docs/configuration.md at main - GitHub
Official implementation for CVPR'23 paper "BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning" - changdaeoh/BlackVIP
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BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning
Mar 26, 2023 · In this work, we propose black-box visual prompting (BlackVIP), which efficiently adapts the PTMs without knowledge about model architectures and parameters. BlackVIP has two components; 1)...
BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning
In this work, we propose black-box visual prompting (BlackVIP), which efficiently adapts the PTMs without knowledge about model architectures and parameters. BlackVIP has two components; 1) Coordinator and 2) simultaneous perturbation stochastic approximation with …
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