MKG-RAG-Bench is a cross-domain benchmark for retrieval in multimodal knowledge graph-augmented generation, constructed via LLM curation from two MKGs with aligned QA datasets.
Yihao Xue, Kristjan Greenewald, Youssef Mroueh, and Baharan Mirzasoleiman
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
SUPERGLASSES is the first VQA benchmark built from actual smart glasses data, and SUPERLENS is an agent using automatic object detection, query decoupling, and multimodal search that outperforms GPT-4o by 2.19% on it.
EnsemHalDet improves VLM hallucination detection by ensembling independent detectors trained on diverse internal states, yielding higher AUC than single-detector baselines across VQA datasets.
citing papers explorer
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MKG-RAG-Bench: Benchmarking Retrieval in Multimodal Knowledge Graph-Augmented Generation
MKG-RAG-Bench is a cross-domain benchmark for retrieval in multimodal knowledge graph-augmented generation, constructed via LLM curation from two MKGs with aligned QA datasets.
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SUPERGLASSES: Benchmarking Vision Language Models as Intelligent Agents for AI Smart Glasses
SUPERGLASSES is the first VQA benchmark built from actual smart glasses data, and SUPERLENS is an agent using automatic object detection, query decoupling, and multimodal search that outperforms GPT-4o by 2.19% on it.
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EnsemHalDet: Robust VLM Hallucination Detection via Ensemble of Internal State Detectors
EnsemHalDet improves VLM hallucination detection by ensembling independent detectors trained on diverse internal states, yielding higher AUC than single-detector baselines across VQA datasets.