与之前只在语言分支学习上下文prompt的方法不同,MaPLe提出了一种联合prompt方法,其中上下文prompt在视觉和语言分支中学习。 具体来说,我们在语言分支中添加可学习上下文标记,并通过耦合函数显式地在语言prompt上设置视觉prompt,以建立它们之间的交互。 为了学习分层上下文表示,我们通过跨不同transformer块的单独可学习上下文pr...
MaPLe: Multi - modal Prompt Learning: Deep Language Prompting:在语言分支中引入可学习的标记来学习语言上下文提示,当深度\(J = 1\)时,退化为CoOp。 Deep Vision Prompting:在视觉分支中引入可学习的标记来学习视觉上下文提示,发现跨阶段共享提示比独立提示更好。 Vision Language Prompt Coupling:通过共享提示来同...
多模态机器学习,英文全称 MultiModal Machine Learning (MMML) 模态(modal)是事情经历和发生的方式,我们生活在一个由多种模态(Multimodal)信息构成的世界,包括视觉信息、听觉信息、文本信息、嗅觉信息等等,当研究的问题或者数据集包含多种这样的模态信息时我们称之为多模态问题,研究多模态问题是推动人工智能更好的了解和...
MaPLe: Multi-modal Prompt Learning [CVPR 2023] MaPLe: Multi-modal Prompt Learning Muhammad Uzair Khattak,Hanoona Rasheed,Muhammad Maaz,Salman Khan,Fahad Shahbaz Khan Official implementation of the paper "MaPLe: Multi-modal Prompt Learning".
In response to this challenge, we present the Multi-modal Attentive Prompt (MAP) learning framework, tailored specifically for few-shot emotion recognition in conversations. The MAP framework consists of four integral modules: multi-modal feature extraction for the sequential embedding of...
我的理解是multimodal指的就是visual words和text两种modal,所以他才说是multimodal的;至于你说的cross-modal我不是很清楚,不能随便乱说。 发布于 2013-05-06 20:24 赞同添加评论 分享收藏喜欢收起 吕阿华 浙江大学 计算机硕士 关注 17 人赞同了该回答 《Retrieving ...
improves the performance of existing multi-modal prompt learning models in few-shot scenarios, exhibiting an average accuracy improvement of 2.31(%) compared to the state-of-the-art methods on 16 shots. Moreover, our methodology exhibits the preeminence in continual learning compared to other ...
《Multi-modal Learning with Missing Modality in Predicting Axillary Lymph Node Metastasis 》 (一)要点 研究背景:多模态学习在医学图像分析中的重要性,尤其是乳腺癌早期患者的腋窝淋巴结转移(ALNM)诊断。 问题陈述:临床信息的收集困难,导致多模态模型在实际应用中受限。
Our work presents an innovative strategy for fusing multi-modal prompts, enhancing performance and adaptability in visual models. 展开 关键词: Multi-modal information fusion Multi-modal prompt Instruction learning Vision transformer DOI: 10.1016/j.inffus.2023.102204 年份: 2024 ...
The role of the sample generation mechanism in contrastive learning is pivotal. It not only determines the pairings of positive and negative samples but also enriches the diversity of the sample pool, thereby substantially affecting the quality of the learned representations. Yet, maintaining semantic ...