pMoE: Prompting Diverse Experts Together Wins More in Visual Adaptation | ScienceToStartup | ScienceToStartup

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References (52)

[1]

A Large-scale Medical Visual Task Adaptation Benchmark

2024Shentong Mo, Xufang Luo et al.

[2]

LSPT: Long-term Spatial Prompt Tuning for Visual Representation Learning

2024Shentong Mo, Yansen Wang et al.

[3]

Revisiting the Parameter Efficiency of Adapters from the Perspective of Precision Redundancy

Founder's Pitch

"pMoE offers a cutting-edge parameter-efficient fine-tuning method by unifying diverse expert domains to enhance visual adaptation tasks."

Visual Adaptation•Score: 7•View PDF ↗

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pMoE: Prompting Diverse Experts Together Wins More in Visual Adaptation

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References (52)

Founder's Pitch

"pMoE offers a cutting-edge parameter-efficient fine-tuning method by unifying diverse expert domains to enhance visual adaptation tasks."

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pMoE: Prompting Diverse Experts Together Wins More in Visual Adaptation

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References (52)

Founder's Pitch

"pMoE offers a cutting-edge parameter-efficient fine-tuning method by unifying diverse expert domains to enhance visual adaptation tasks."

Commercial Viability Breakdown

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