prompt4 [paper review] (VPT) Visual Prompt Tuning Visual Prompt Tuning - ECCV 2022https://arxiv.org/abs/2203.12119 Visual Prompt TuningThe current modus operandi in adapting pre-trained models involves updating all the backbone parameters, ie, full fine-tuning. This paper introduces Visual Prompt Tuning (VPT) as an efficient and effective alternative to full fine-tuning for large-scale Trarxiv.org1. Prompt TuingPrompting- The promising approach.. 2024. 7. 21. [논문 리뷰] (SHIP) Improving Zero-Shot Generalization for CLIP with Synthesized Prompts Improving Zero-Shot Generalization for CLIP with Synthesized Prompts - ICCV 2023이번에는 Co-CoOp의 방식을 차용한 논문에 대해 리뷰하도록 하겠습니다.https://arxiv.org/abs/2307.07397 Improving Zero-Shot Generalization for CLIP with Synthesized PromptsWith the growing interest in pretrained vision-language models like CLIP, recent research has focused on adapting these models to downstream tasks. Despite achieving promising .. 2024. 7. 21. [논문 리뷰] (Co-CoOp) Conditional Prompt Learning for Vision-Language Models Conditional Prompt Learning for Vision-Language Models - CVPR 2022 https://arxiv.org/abs/2203.05557v2 Conditional Prompt Learning for Vision-Language ModelsWith the rise of powerful pre-trained vision-language models like CLIP, it becomes essential to investigate ways to adapt these models to downstream datasets. A recently proposed method named Context Optimization (CoOp) introduces the concept.. 2024. 7. 20. [논문 리뷰] (CoOp) Learning to Prompt for Vision-Language Models - IJCV 2022 Learning to Prompt for Vision-Language Models - IJCV 2022https://arxiv.org/abs/2109.01134 Learning to Prompt for Vision-Language ModelsLarge pre-trained vision-language models like CLIP have shown great potential in learning representations that are transferable across a wide range of downstream tasks. Different from the traditional representation learning that is based mostly on discretiarxiv.o.. 2024. 7. 20. 이전 1 다음
