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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 81–90 of 95 papers

TitleStatusHype
BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual QuestionsCode2
Domain Adaptation via Minimax Entropy for Real/Bogus Classification of Astronomical Alerts—0
Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative InstructionsCode2
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language ModelsCode2
Multi-Modal Evaluation Approach for Medical Image Segmentation—0
MAAL: Multimodality-Aware Autoencoder-Based Affordance Learning for 3D Articulated ObjectsCode0
Masked Motion Encoding for Self-Supervised Video Representation LearningCode1
MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature DistributionCode0
MME-CRS: Multi-Metric Evaluation Based on Correlation Re-Scaling for Evaluating Open-Domain Dialogue—0
Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array—0
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