A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Bo Li, Yuanhan Zhang, Liangyu Chen, et al.
Otter is a multi-modal model instruction-tuned with both textual and visual in-context examples, supported by the MIMIC-IT dataset of over 3 million instruction-response pairs.
Tadas Baltrušaitis, Chaitanya Ahuja, Louis‐Philippe Morency
This survey proposes a taxonomy of five core challenges in multimodal machine learning—representation, translation, alignment, fusion, and co-learning—moving beyond early/late fusion categorization.
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This paper surveys the evolution of foundation models from large language models to large multimodal models, tracing historical progress and outlining future prospects.
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TechRAG proposes an evidence-gated multimodal agentic RAG architecture for technical literature reasoning, motivated by limitations of basic RAG.
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HAM-RAG formulates structure-faithful multimodal RAG and introduces HAM-Bench to evaluate hierarchy-aware interleaved generation.
Zhan-Lun Chang, Dong-Jun Han, Seyyedali Hosseinalipour, et al.
A two-stage multimodal RAG framework that separates multimodal query translation from generator-aligned reranking to bridge the semantic-utility gap.
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An agentic multimodal RAG framework for nuclear regulatory review achieves 69.0% accuracy, outperforming Unified Multimodal RAG by 6.5%.
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Proposes a multi-granularity context-enhanced RAG framework that retrieves over multimodal knowledge graphs to improve multimodal reasoning.
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RiverEcho-2.0 presents a real-time interactive system for Yellow River culture using enhanced multimodal document RAG to retrieve tables, texts, and images.
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The paper proposes a multimodal agentic RAG framework to integrate multi-source heterogeneous information in higher education institutions.
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MKG-RAG-Bench is a benchmark for evaluating retrieval in multimodal knowledge graph-augmented generation, addressing the gap left by unstructured multimodal RAG.
Seongjun Ha, Md Rashedul Islam, Gaurav Nanda, et al.
A multimodal RAG system for Cessna 172 maintenance manuals reduces technician search burden by retrieving text and diagrams.