论文地址：MultimodalPromptLearningwithMissingModalitiesforSentimentAnalysisandEmotionRecognition-ACLAnthology代码地址：GitHub-zrguo/MPLMM:[ACL2024Main]OfficialPyTorchimplementationofthepaper"MultimodalPromptLearningwithMissingMo

ASurveyonMultimodalLargeLanguageModelshttps://arxiv.org/pdf/2306.13549多模态大预言模型，其是基于LLM，同时具有了接收、推理、输出多模态信息的能力。Inlightofthiscomplementarity,LLMandLVMruntowardseachother,leadingtothenewfieldofMultimodalL

DocKylin:ALargeMultimodalModelforVisualDocumentUnderstandingwithEfficientVisualSlimmingarxiv:http://arxiv.org/abs/2406.19101视觉处理器+LLM：视觉处理器：SwinTransformer创新点：通过：1、去除图片冗余像素；2、去除冗余token。来减小模型中的视觉处理器的参数量

Howtopassmultimodaldatadirectlytomodelshttps://python.langchain.com/v0.2/docs/how_to/multimodal_inputs/Herewedemonstratehowtopassmultimodalinputdirectlytomodels.WecurrentlyexpectallinputtobepassedinthesameformatasOpenAIe

首先解释这篇工作名称Palm-E，发表时间为2023.03，其中的Palm是谷歌内部在2022.04开发的大语言模型，功能类似ChatGPT，只是由于各种原因没有那样火起来，E是Embodied的首字母，翻译过来就是具身多模态大语言模型大模型，我们一般习惯将其称为具身多模态大模型。何为具身？这个词听起来非常

1.Initially,Ithoughtthatdigitalmultimodalwritingsimplycombinestraditionaltextwritingwithmultimediaelementssuchasimages,audio,video,etc.However,asIexploredthisfieldmoredeeply,Icametorealizethatdigitalmultimodalwritingis

ReflectiveJournalFinal1.Atthebeginningofthecourse,Ijusthaveablurryunderstandingofdigitalmultimodalcomposing.Afterhavingcoursesformanytimes,Igraduallygraspedtheconceptofdigitalmultimodalcomposing.Thekeyresidesin“multim

Firstofall,Iwouldliketothankmyteachers,LiuFulanandZhouMengchen,fortheirguidancethroughoutthesemester.Iamalsoverygratefultotheteachersforgivingmethisopportunitytolearndigitalmultimodalwritingsystematically.AlthoughI

郑重声明：原文参见标题，如有侵权，请联系作者，将会撤销发布！ Abstract大型多模态模型(LMM)将大语言模型扩展到视觉领域。最初的LMM使用整体图像和文本提示词来生成无定位的文本响应。最近，区域级LMM已被用于生成视觉定位响应。然而，它们仅限于一次仅引用单个目标类别，要求用户指定

目录概符号说明MotivationLGMRecLocalGraphEmbeddingGlobalGraphEmbeddingFusion代码GuoZ.,LiJ.,LiG.,WangC.,ShiS.andRuanB.LGMRec:Localandglobalgraphlearningformultimodalrecommendation.AAAI,2024.概本文采用分解的方法进行对ID和模态信

Inthepasttwoweeks,Ihavelearnedtoinfertheemotionsofcharactersfromdetails.Forexample,inthefirstclass,weobservedtheprotagonist'semotionsthroughthedetailsofthevideo"TheNecklace",anddiscoveredherunwillingnes

Duringthetwoweeksofstudy,Ihavegainedadeeperunderstandingofmultimodalwriting,whichisacombinationoftext,images,audioandvideosoastoexpressinformationmorecomprehensivelyandunderstandthecontentmoreintuitively.Besides,Iha

目录概FREEDOMMotivationFrozenItem-ItemgraphDenoisingUser-ItemBipartiteGraphTwoGraphsforLearning代码ZhouX.andShenZ.Ataleoftwographs:Freezinganddenoisinggraphstructuresformultimodalrecommendation.概本文主要是对LATTICE的改进.FREE

概述提出了InstructRL，包含一个multimodaltransformer用来将视觉obs和语言的instruction进行编码，以及一个transformer-basedpolicy，可以基于编码的表示来输出actions。前者在1M的image-text对和NL的text上进行训练，后者跟踪了整个obs和act的历史，自回归地输出动作。问题纯语言

一、摘要随着社交媒体的快速发展，假新闻已经成为一个重大的社会问题，它无法通过人工调查及时解决。这激发了大量关于自动假新闻检测的研究。大多数研究探索了基于新闻记录

论文解读：MultimodalMachineTranslationwithEmbeddingPrediction  机器翻译中有一个非常重要的问题即是对未知词（unknownword）和罕见词（rareword）的预测。有许多工作着

WangQ,ZhanL,ThompsonP,etal.Multimodallearningwithincompletemodalitiesbyknowledgedistillation[C]//Proceedingsofthe26thACMSIGKDDInternatio