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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 51–75 of 95 papers

TitleStatusHype
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
DynTok: Dynamic Compression of Visual Tokens for Efficient and Effective Video Understanding—0
EACO: Enhancing Alignment in Multimodal LLMs via Critical Observation—0
The economic value of empowering older patients transitioning from hospital to home: Evidence from the 'Your Care Needs You' intervention—0
Enhancing Instruction-Following Capability of Visual-Language Models by Reducing Image Redundancy—0
Enhancing the Spatial Awareness Capability of Multi-Modal Large Language Model—0
Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models—0
EvoMoE: Expert Evolution in Mixture of Experts for Multimodal Large Language Models—0
Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering—0
GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors—0
Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment—0
Improving LLM Video Understanding with 16 Frames Per Second—0
Language-Vision Planner and Executor for Text-to-Visual Reasoning—0
Learning Multilingual Meta-Embeddings for Code-Switching Named Entity Recognition—0
Logic-in-Frames: Dynamic Keyframe Search via Visual Semantic-Logical Verification for Long Video Understanding—0
Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array—0
Mitigating Hallucinations in Large Vision-Language Models with Internal Fact-based Contrastive Decoding—0
Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models—0
MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency—0
MME-CRS: Multi-Metric Evaluation Based on Correlation Re-Scaling for Evaluating Open-Domain Dialogue—0
MME-Finance: A Multimodal Finance Benchmark for Expert-level Understanding and Reasoning—0
MME-Industry: A Cross-Industry Multimodal Evaluation Benchmark—0
MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?—0
MME-Reasoning: A Comprehensive Benchmark for Logical Reasoning in MLLMs—0
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