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MME

MME is a comprehensive evaluation benchmark for multimodal large language models. It measures both perception and cognition abilities on a total of 14 subtasks, including existence, count, position, color, poster, celebrity, scene, landmark, artwork, OCR, commonsense reasoning, numerical calculation, text translation, and code reasoning.

Papers

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Showing 51–60 of 95 papers

TitleStatusHype
A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise—0
AIDE: Agentically Improve Visual Language Model with Domain Experts—0
An LMM for Efficient Video Understanding via Reinforced Compression of Video Cubes—0
Apollo: An Exploration of Video Understanding in Large Multimodal Models—0
Benchmarking and In-depth Performance Study of Large Language Models on Habana Gaudi Processors—0
DAMRO: Dive into the Attention Mechanism of LVLM to Reduce Object Hallucination—0
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin—0
Domain Adaptation via Minimax Entropy for Real/Bogus Classification of Astronomical Alerts—0
Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models—0
DrVideo: Document Retrieval Based Long Video Understanding—0
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