MLLM的主流paradigm可以分为四个主要类型:Multimodal Instruction Tuning (M-IT)、Multimodal In-Context Learning (M-ICL)、Multimodal Chain-of-Thought (M-CoT)和LLM-Aided Visual Reasoning (LAVR)。这些paradigm代表了MLLM的基本技术和应用领域。 Multimodal Instruction Tuning Preliminaries Multimodal Instruction Tu...
在GPT-3[15]中, 所有任务都可以被统一建模, 任务描述与任务输入视为语言模型的历史上下文, 而输出则为语言模型需要预测的未来信息, 通过给予模型一些提示语, 让模型根据提示语来生成所需要的输出, 这种方式也被称为是情景学习(in-context learning). Prefix-Tuning[16]摒弃了人工设计模板或自动化搜索模板的方式, ...
如In-context demonstrations中示例的多样性和示例质量等因素可能提高模型输出的质量。在多模态基础模型的背景下,像Flamingo和BLIP-2这样的模型,仅给出少量示例,就在各种视觉理解任务上表现出色。通过在环境中的Agents采取某些行动时合并环境特定的反馈,可以进一步改进 In-context Learning。简评:example的多样性和质量影响I...
Allows learners to apply the teaching to real-life situations.When learners can immediately apply learning in a real-world context, they’re not only using kinesthetic learning, but they improve their retention of the material and are better able to solve real-world problems with it. ...
Engaging students through multimodal learning environments: An Indonesian context. The 4th International Conference on Language, Society and Culture in Asian Context, KnE Social Science (pp. 202-209).Sankey, M., Birch, D., & Gardiner, M. (2010). Engaging students through multimodal learning ...
论文《Learning Alignment for Multimodal Emotion Recognition from Speech》,作者Haiyang Xu(DiDi Chuxing, Beijing, China),经典的多模态情绪识别(语音和文本相结合)论文。 2. 摘要 语音情绪识别是一个具有挑战性的问题,因为人类以微妙而复杂的方式传达情感。为了对人类语音进行情感识别,可以从音频信号中提取与情感相...
context presentinthe image.Pay close attention toanynumbers,data,orquantitative information visible,\andbe sure to include those numerical values alongwiththeir semantic meaninginyour description.\ Thoroughly readandinterpret the entire image before providing your detailed caption describing the \ ...
Consequently, vocabulary learning is considered fundamental and has been emphasized in elementary school. In the EFL context in Taiwan, children usually start learning vocabulary from textbooks in mandatory English classes (Shadiev et al., 2020 Method The case study (Peterson, 2010) went through two...
3.2. Multimodal In-Context Learning 对于LLM,ICL是一个很重要的能力。ICL有两个很好的特点:(1)与传统的监督学习范式从丰富的数据中学习隐含模式不同,ICL的关键在于从analogy中学习[74]。具体来说,在ICL环境中,llm从几个例子中学习,并附带一个可选的指令,并推断出新的问题,从而以few shot的方式解决复杂和未知...
2.多模态上下文学习 (M-ICL:Multimodal In-Context Learning) 3.多模态思维链(M-CoT:Multimodal Chain of Thought) 4.LLM 辅助视觉推理 (LAVR:LLM-Aided Visual Reasoning) 实验分析 可改进的地方 文献介绍 题目:A Survey on Multimodal Large Language Models 作者:Shukang Yin1* , Chaoyou Fu2∗‡†...