Introduces V-RAGBench benchmark and CARVE method that selects per-chunk retrieval configurations via parallel retrievers and adaptive reranking, outperforming eight VideoRAG baselines.
Advancing egocentric video question answering with multimodal large language models.arXiv preprint arXiv:2504.04550, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Fine-tuned VLMs guided by eye gaze and ego motion achieve 14.5% accuracy improvement over a transformer baseline for egocentric pedestrian intent decoding.
citing papers explorer
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Rethinking RAG in Long Videos: What to Retrieve and How to Use It?
Introduces V-RAGBench benchmark and CARVE method that selects per-chunk retrieval configurations via parallel retrievers and adaptive reranking, outperforming eight VideoRAG baselines.
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Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models
Fine-tuned VLMs guided by eye gaze and ego motion achieve 14.5% accuracy improvement over a transformer baseline for egocentric pedestrian intent decoding.