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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 1–25 of 95 papers

TitleStatusHype
VideoEval-Pro: Robust and Realistic Long Video Understanding EvaluationCode4
Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language ModelsCode4
Long Context Transfer from Language to VisionCode4
Lyra: An Efficient and Speech-Centric Framework for Omni-CognitionCode3
Flash-VStream: Efficient Real-Time Understanding for Long Video StreamsCode3
MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMsCode3
TimeChat-Online: 80% Visual Tokens are Naturally Redundant in Streaming VideosCode3
Video-RAG: Visually-aligned Retrieval-Augmented Long Video ComprehensionCode3
L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object DetectionCode2
Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention CausalityCode2
QuoTA: Query-oriented Token Assignment via CoT Query Decouple for Long Video ComprehensionCode2
Honeybee: Locality-enhanced Projector for Multimodal LLMCode2
MMICL: Empowering Vision-language Model with Multi-Modal In-Context LearningCode2
Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive DecodingCode2
High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement LearningCode2
VideoDeepResearch: Long Video Understanding With Agentic Tool UsingCode2
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language ModelsCode2
SpaceR: Reinforcing MLLMs in Video Spatial ReasoningCode2
BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual QuestionsCode2
Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative InstructionsCode2
VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long VideosCode2
Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference OptimizationCode1
Prompt Highlighter: Interactive Control for Multi-Modal LLMsCode1
Masked Motion Encoding for Self-Supervised Video Representation LearningCode1
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language ModelsCode1
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