Cross-Modal Prompt Tuning - Code World

Cross-Modal Prompt Tuning

Enterprise 2023-08-27 03:57:00 views: null

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Origin blog.csdn.net/qq_43775680/article/details/127188496

Cross-Modal Prompt Tuning

prompt tuning

CVPR'2023 | Cross-modal Adaptation: A new paradigm for fine-tuning based on CLIP

Prompt Paradigm Industry Practice Sharing! Realization of cross-modal document information extraction based on Paddle UIE-X and Intel OpenVINO

Cross-modal retrieval paper reading: (PTP)Position-guided Text Prompt for Vision-Language Pre-training

Visual Prompt Tuning

Cross-modal learning

【LLM】Prompt tuning large model fine-tuning practice

In which scenarios does Prompt Tuning perform better than Fine Tuning?

Cross-modal retrieval paper reading: IMRAM

[Review] Cross-modal trustworthy perception

Paper reading: Adversarial Cross-Modal Retrieval Adversarial Cross-Modal Retrieval

LLM: New paradigm of Prompt-Tuning/Instruction-tuning fine-tuning

Overview of the principles of efficient fine-tuning technology for large model parameters (2) - BitFit, Prefix Tuning, Prompt Tuning

Summary of fine-tuning methods for large models: LoRA, Adapter, Prefix-tuning, P-tuning, Prompt-tuning

On the overview of efficient fine-tuning of large models: Adapter Tuning, AdaMix, PET, Prefix-Tuning, Prompt Tuning, P-tuning, P-tuning v2

[Presentation] HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification

Classic Review | A New Approach to Cross-Modal Multimedia Retrieval

Cross-modal text reasoning and generation based on deep learning

Cross-Modal Complementary Network with Hierarchical Fusion for Multimodal Sentiment Classification

Paper Notes: Probabilistic Embeddings for Cross-Modal Retrieval

Paper Study｜A review of the development of generative cross-modal steganography

Summary of the latest top conference papers on cross-modal retrieval in 2023

[Instruction fine-tuning of LLM series] Long story short, "Prompt" for instruction fine-tuning of large models

P-Tuning v2: Prompt optimization equal to fine-tuning performance

Cross-modal retrieval paper reading: Cross Modal Retrieval with Querybank Normalization

Summary of three fine-tuning techniques for pre-training large language models: fine-tuning, parameter-efficient fine-tuning and prompt-tuning

（四十九）：UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning

Pachyderm cross-modal multi-modal data fusion and game design

Cross-modales Prompt-Tuning

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