KG-MM-Survey

KG-MM-Survey

知识图谱与多模态学习融合研究综述

本项目汇总了知识图谱与多模态学习融合研究的相关论文,主要包括知识图谱驱动的多模态学习(KG4MM)和多模态知识图谱(MM4KG)两个方向。KG4MM探讨知识图谱对多模态任务的支持,MM4KG研究多模态技术在知识图谱领域的应用。项目覆盖理解推理、分类、生成、检索等多种任务,提供了详细的文献列表和资源。这是一份系统全面的知识图谱与多模态学习交叉领域研究综述。

知识图谱多模态学习视觉问答知识融合深度学习Github开源项目

KG-MM-Survey

Awesome License: MIT

Task

🙌 This repository collects papers integrating Knowledge Graphs (KGs) and Multi-Modal Learning, focusing on research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm.

😎 Welcome to recommend missing papers through Adding Issues or Pull Requests.

<details> <summary>👈 🔎 Roadmap </summary>

Roadmap

</details>

🔔 News

Todo:

    • Finish updating papers

📜 Content


🤖🌄 KG-driven Multi-modal Learning (KG4MM)

Understanding & Reasoning Tasks

<details> <summary>👈 🔎 Pipeline </summary>

KG4MMR

</details>

Visual Question Answering

<details> <summary>👈 🔎 Benchmarks </summary>

VQA

</details>
  • [arXiv 2024] Knowledge Condensation and Reasoning for Knowledge-based VQA.
  • [arXiv 2024] VCD: Knowledge Base Guided Visual Commonsense Discovery in Images.
  • [arXiv 2024] Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment.
  • [ACL 2024] Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering.
  • [arXiv 2024] II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question Answering.
  • [arXiv 2024] Knowledge Generation for Zero-shot Knowledge-based VQA.
  • [arXiv 2024] GeReA: Question-Aware Prompt Captions for Knowledge-based Visual Question Answering.
  • [arXiv 2024] Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation.
  • [AAAI 2024] BOK-VQA: Bilingual outside Knowledge-Based Visual Question Answering via Graph Representation Pretraining.
  • [arXiv 2024] Cross-modal Retrieval for Knowledge-based Visual Question Answering.
  • [TMM 2024] Learning to Supervise Knowledge Retrieval over a Tree Structure for Visual Question Answering.
  • [MTA 2024] Hierarchical Attention Networks for Fact-based Visual Question Answering.
  • [KAIS 2024] Knowledge enhancement and scene understanding for knowledge-based visual question answering.
  • [arXiv 2023] Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering.
  • [arXiv 2023] Open-Set Knowledge-Based Visual Question Answering with Inference Paths.
  • [arXiv 2023] Prompting Vision Language Model with Knowledge from Large Language Model for Knowledge-Based VQA.
  • [EMNLP 2023] Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts.
  • [EMNLP 2023] A Simple Baseline for Knowledge-Based Visual Question Answering.
  • [EMNLP 2023] MM-Reasoner: A Multi-Modal Knowledge-Aware Framework for Knowledge-Based Visual Question Answering.
  • [NeurIPS 2023] LoRA: A Logical Reasoning Augmented Dataset for Visual Question Answering.
  • [CVPR 2023] Prompting Large Language Models with Answer Heuristics for Knowledge-Based Visual Question Answering.
  • [EACL 2023] FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering.
  • [WACV 2023] VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge.
  • [ICASSP 2023] Outside Knowledge Visual Question Answering Version 2.0.
  • [ICME 2023] A Retriever-Reader Framework with Visual Entity Linking for Knowledge-Based Visual Question Answering.
  • [TIP 2023] Semantic-Aware Modular Capsule Routing for Visual Question Answering.
  • [ACM MM 2023] AI-VQA: Visual Question Answering based on Agent Interaction with Interpretability.
  • [SIGIR 2023] A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering.
  • [ICMR 2023] Explicit Knowledge Integration for Knowledge-Aware Visual Question Answering about Named Entities.
  • [TMM 2023] Resolving Zero-shot and Fact-based Visual Question Answering via Enhanced Fact Retrieval.
  • [ESA 2023] Image captioning for effective use of language models in knowledge-based visual question answering.
  • [EMNLP 2022] Retrieval Augmented Visual Question Answering with Outside Knowledge.
  • [EMNLP 2022] Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering.
  • [IJCKG 2022] LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection.
  • [NeurIPS 2022] REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering.
  • [CVPR 2022] MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering.
  • [CVPR 2022] Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering.
  • [ECCV 2022] A-OKVQA: A Benchmark for Visual Question Answering Using World Knowledge.
  • [ICCV 2022] VQA-GNN: Reasoning with Multimodal Semantic Graph for Visual Question Answering.
  • [AAAI 2022] Dynamic Key-Value Memory Enhanced Multi-Step Graph Reasoning for Knowledge-Based Visual Question Answering.
  • [AAAI 2022] An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA.
  • [ACM MM 2022] A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQA.
  • [ACL 2022] Hypergraph Transformer: Weakly-Supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering.
  • [WWW 2022] Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection.
  • [SITIS 2022] Multimodal Knowledge Reasoning for Enhanced Visual Question Answering.
  • [KBS 2022] Fact-based visual question answering via dual-process system.
  • [ISWC 2021] Zero-Shot Visual Question Answering Using Knowledge Graph.
  • [ISWC 2021] Graphhopper: Multi-hop Scene Graph Reasoning for Visual Question Answering.
  • [ACL 2021] In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering.
  • [KDD 2021] Select, Substitute, Search: A New Benchmark for Knowledge-Augmented Visual Question Answering.
  • [CVPR 2021] KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA.
  • [PR 2021] Knowledge base graph embedding module design for Visual question answering model.
  • [SIGIR 2021] Passage Retrieval for Outside-Knowledge Visual Question Answering.
  • [TNNLS 2021] Rich Visual Knowledge-Based Augmentation Network for Visual Question Answering.
  • [COLING 2020] Towards Knowledge-Augmented Visual Question Answering.
  • [arXiv 2020] Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings.
  • [ACM MM 2020] Boosting Visual Question Answering with Context-aware Knowledge Aggregation.
  • [EMNLP 2020] ConceptBert: Concept-Aware Representation for Visual Question Answering.
  • [PR 2020] Cross-modal knowledge reasoning for knowledge-based visual question answering.
  • [IJCAI 2020] Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering.
  • [AAAI 2020] KnowIT VQA: Answering Knowledge-Based Questions about Videos.
  • [AAAI 2019] KVQA: Knowledge-Aware Visual Question Answering.
  • [CVPR 2019] OK-VQA: Visual Question Answering Benchmark Requiring External Knowledge.
  • [NeurIPS 2018] Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering.
  • [ECCV 2018] Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering.
  • [CVPR 2018] Learning Visual Knowledge Memory Networks for Visual Question Answering.
  • [KDD 2018] R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question

编辑推荐精选

博思AIPPT

博思AIPPT

AI一键生成PPT,就用博思AIPPT!

博思AIPPT,新一代的AI生成PPT平台,支持智能生成PPT、AI美化PPT、文本&链接生成PPT、导入Word/PDF/Markdown文档生成PPT等,内置海量精美PPT模板,涵盖商务、教育、科技等不同风格,同时针对每个页面提供多种版式,一键自适应切换,完美适配各种办公场景。

热门AI工具AI办公办公工具智能排版AI生成PPT博思AIPPT海量精品模板AI创作
潮际好麦

潮际好麦

AI赋能电商视觉革命,一站式智能商拍平台

潮际好麦深耕服装行业,是国内AI试衣效果最好的软件。使用先进AIGC能力为电商卖家批量提供优质的、低成本的商拍图。合作品牌有Shein、Lazada、安踏、百丽等65个国内外头部品牌,以及国内10万+淘宝、天猫、京东等主流平台的品牌商家,为卖家节省将近85%的出图成本,提升约3倍出图效率,让品牌能够快速上架。

iTerms

iTerms

企业专属的AI法律顾问

iTerms是法大大集团旗下法律子品牌,基于最先进的大语言模型(LLM)、专业的法律知识库和强大的智能体架构,帮助企业扫清合规障碍,筑牢风控防线,成为您企业专属的AI法律顾问。

SimilarWeb流量提升

SimilarWeb流量提升

稳定高效的流量提升解决方案,助力品牌曝光

稳定高效的流量提升解决方案,助力品牌曝光

Sora2视频免费生成

Sora2视频免费生成

最新版Sora2模型免费使用,一键生成无水印视频

最新版Sora2模型免费使用,一键生成无水印视频

Transly

Transly

实时语音翻译/同声传译工具

Transly是一个多场景的AI大语言模型驱动的同声传译、专业翻译助手,它拥有超精准的音频识别翻译能力,几乎零延迟的使用体验和支持多国语言可以让你带它走遍全球,无论你是留学生、商务人士、韩剧美剧爱好者,还是出国游玩、多国会议、跨国追星等等,都可以满足你所有需要同传的场景需求,线上线下通用,扫除语言障碍,让全世界的语言交流不再有国界。

讯飞绘文

讯飞绘文

选题、配图、成文,一站式创作,让内容运营更高效

讯飞绘文,一个AI集成平台,支持写作、选题、配图、排版和发布。高效生成适用于各类媒体的定制内容,加速品牌传播,提升内容营销效果。

AI助手热门AI工具AI创作AI辅助写作讯飞绘文内容运营个性化文章多平台分发
TRAE编程

TRAE编程

AI辅助编程,代码自动修复

Trae是一种自适应的集成开发环境(IDE),通过自动化和多元协作改变开发流程。利用Trae,团队能够更快速、精确地编写和部署代码,从而提高编程效率和项目交付速度。Trae具备上下文感知和代码自动完成功能,是提升开发效率的理想工具。

热门AI工具生产力协作转型TraeAI IDE
商汤小浣熊

商汤小浣熊

最强AI数据分析助手

小浣熊家族Raccoon,您的AI智能助手,致力于通过先进的人工智能技术,为用户提供高效、便捷的智能服务。无论是日常咨询还是专业问题解答,小浣熊都能以快速、准确的响应满足您的需求,让您的生活更加智能便捷。

imini AI

imini AI

像人一样思考的AI智能体

imini 是一款超级AI智能体,能根据人类指令,自主思考、自主完成、并且交付结果的AI智能体。

下拉加载更多