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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 76–95 of 95 papers

TitleStatusHype
RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models—0
Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models—0
Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language ModelsCode0
A Challenger to GPT-4V? Early Explorations of Gemini in Visual ExpertiseCode0
Silkie: Preference Distillation for Large Visual Language Models—0
ShareGPT4V: Improving Large Multi-Modal Models with Better CaptionsCode0
The Use of Symmetry for Models with Variable-size Variables—0
Enhancing the Spatial Awareness Capability of Multi-Modal Large Language Model—0
Benchmarking and In-depth Performance Study of Large Language Models on Habana Gaudi Processors—0
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and CompositionCode0
Domain Adaptation via Minimax Entropy for Real/Bogus Classification of Astronomical Alerts—0
Multi-Modal Evaluation Approach for Medical Image Segmentation—0
MAAL: Multimodality-Aware Autoencoder-Based Affordance Learning for 3D Articulated ObjectsCode0
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
Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation—0
Learning Multilingual Meta-Embeddings for Code-Switching Named Entity Recognition—0
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin—0
Scalable K-Medoids via True Error Bound and Familywise Bandits—0
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