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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 51–95 of 95 papers

TitleStatusHype
Temporal Reasoning Transfer from Text to Video—0
Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention CausalityCode2
DAMRO: Dive into the Attention Mechanism of LVLM to Reduce Object Hallucination—0
TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable QuestionsCode0
ParGo: Bridging Vision-Language with Partial and Global ViewsCode1
MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?—0
L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object DetectionCode2
Decoding Multilingual Moral Preferences: Unveiling LLM's Biases Through the Moral Machine ExperimentCode0
Long Context Transfer from Language to VisionCode4
DrVideo: Document Retrieval Based Long Video Understanding—0
Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video AnalysisCode1
VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long VideosCode2
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
Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive DecodingCode2
Pensieve: Retrospect-then-Compare Mitigates Visual HallucinationCode1
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language ModelsCode1
A Challenger to GPT-4V? Early Explorations of Gemini in Visual ExpertiseCode0
Silkie: Preference Distillation for Large Visual Language Models—0
Honeybee: Locality-enhanced Projector for Multimodal LLMCode2
Prompt Highlighter: Interactive Control for Multi-Modal LLMsCode1
Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference OptimizationCode1
ShareGPT4V: Improving Large Multi-Modal Models with Better CaptionsCode0
The Use of Symmetry for Models with Variable-size Variables—0
What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction TuningCode1
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
MMICL: Empowering Vision-language Model with Multi-Modal In-Context LearningCode2
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
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
Semi-supervised Domain Adaptation via Minimax EntropyCode1
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