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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 26–50 of 95 papers

TitleStatusHype
Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language BootstrappingCode1
To Preserve or To Compress: An In-Depth Study of Connector Selection in Multimodal Large Language ModelsCode1
ParGo: Bridging Vision-Language with Partial and Global ViewsCode1
Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video AnalysisCode1
Pensieve: Retrospect-then-Compare Mitigates Visual HallucinationCode1
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language ModelsCode1
Prompt Highlighter: Interactive Control for Multi-Modal LLMsCode1
Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference OptimizationCode1
What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction TuningCode1
Masked Motion Encoding for Self-Supervised Video Representation LearningCode1
Semi-supervised Domain Adaptation via Minimax EntropyCode1
Q-Frame: Query-aware Frame Selection and Multi-Resolution Adaptation for Video-LLMs—0
Language-Vision Planner and Executor for Text-to-Visual Reasoning—0
DynTok: Dynamic Compression of Visual Tokens for Efficient and Effective Video Understanding—0
Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering—0
EvoMoE: Expert Evolution in Mixture of Experts for Multimodal Large Language Models—0
MME-Reasoning: A Comprehensive Benchmark for Logical Reasoning in MLLMs—0
Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models—0
Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models—0
VISTA: Enhancing Vision-Text Alignment in MLLMs via Cross-Modal Mutual Information Maximization—0
Visual Instruction Tuning with Chain of Region-of-Interest—0
An LMM for Efficient Video Understanding via Reinforced Compression of Video Cubes—0
MME-Unify: A Comprehensive Benchmark for Unified Multimodal Understanding and Generation Models—0
Instruction-Aligned Visual Attention for Mitigating Hallucinations in Large Vision-Language ModelsCode0
Improving LLM Video Understanding with 16 Frames Per Second—0
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