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Status: Unchecked ×Keyword: cross-task generalization ×Clear all

2 claims from 1 paper

1. Computer Science › Multimodal Machine Learning Applications

   Multitask Vision-Language Prompt Tuning

   Shen, Yang, Zhang et al. · IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · 2024

   The paper proposes multitask vision-language prompt tuning, which shares knowledge across tasks when adapting vision-language models, and reports gains over single-task prompt tuning on 20 vision tasks.

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   1. UncheckedA single prompt learned from several source tasks can start off each target task, and many target tasks can share prompt vectors and be learned together.“Specifically, (i) we demonstrate the effectiveness of learning a single transferable prompt from multiple source tasks to initialize the prompt for each target task; (ii) we show many target tasks can benefit each other from sharing prompt vectors and thus can be jointly learned via multitask promp…”
   2. UncheckedAcross 20 vision tasks, multitask prompt tuning beats single-task prompt tuning baselines and sets new state-of-the-art on few-shot ELEVATER and cross-task benchmarks.“Results in 20 vision tasks demonstrate that the proposed approach outperforms all single-task baseline prompt tuning methods, setting the new state-of-the-art on the few-shot ELEVATER benchmarks and cross-task generalization benchmarks.”