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Visual Question Answering

MLLM Leaderboard

Papers

Showing 150 of 2177 papers

TitleStatusHype
Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model ScalingCode11
JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and GenerationCode11
Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and GenerationCode11
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any ResolutionCode11
SWIFT:A Scalable lightWeight Infrastructure for Fine-TuningCode11
RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V TrustworthinessCode11
DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal UnderstandingCode9
CogVLM2: Visual Language Models for Image and Video UnderstandingCode9
Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language ModelsCode9
LLaVA-CoT: Let Vision Language Models Reason Step-by-StepCode7
mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language ModelsCode7
Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative PretrainingCode7
Chameleon: Mixed-Modal Early-Fusion Foundation ModelsCode7
Mini-Gemini: Mining the Potential of Multi-modality Vision Language ModelsCode7
DeepSeek-VL: Towards Real-World Vision-Language UnderstandingCode7
SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language ModelsCode7
MoE-LLaVA: Mixture of Experts for Large Vision-Language ModelsCode7
MiniGPT-v2: large language model as a unified interface for vision-language multi-task learningCode7
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language ModelsCode7
RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human FeedbackCode6
Improved Baselines with Visual Instruction TuningCode6
An Empirical Study of Scaling Instruct-Tuned Large Multimodal ModelsCode6
Visual Instruction TuningCode6
GPT-4 Technical ReportCode6
Show-o: One Single Transformer to Unify Multimodal Understanding and GenerationCode5
VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language TasksCode5
Wings: Learning Multimodal LLMs without Text-only ForgettingCode5
Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of ExpertsCode5
CogAgent: A Visual Language Model for GUI AgentsCode5
CogVLM: Visual Expert for Pretrained Language ModelsCode5
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and BeyondCode5
MMBench: Is Your Multi-modal Model an All-around Player?Code5
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction ModelCode5
Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal LearningCode4
OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action ModelCode4
A Survey on Vision-Language-Action Models for Embodied AICode4
OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual ReasoningCode4
The All-Seeing Project V2: Towards General Relation Comprehension of the Open WorldCode4
TinyLLaVA: A Framework of Small-scale Large Multimodal ModelsCode4
OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLMCode4
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language ModelsCode4
Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional TokenizationCode4
GPT-4V(ision) is a Generalist Web Agent, if GroundedCode4
VILA: On Pre-training for Visual Language ModelsCode4
Video-LLaVA: Learning United Visual Representation by Alignment Before ProjectionCode4
SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language ModelsCode4
OtterHD: A High-Resolution Multi-modality ModelCode4
mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality CollaborationCode4
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language ModelsCode4
MIMIC-IT: Multi-Modal In-Context Instruction TuningCode4
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MMCTAgent (GPT-4 + GPT-4V)GPT-4 score74.24Unverified
2Qwen2-VL-72BGPT-4 score74Unverified
3InternVL2.5-78BGPT-4 score72.3Unverified
4GPT-4o +text rationale +IoTGPT-4 score72.2Unverified
5Lyra-ProGPT-4 score71.4Unverified
6GLM-4V-PlusGPT-4 score71.1Unverified
7Phantom-7BGPT-4 score70.8Unverified
8InternVL2.5-38BGPT-4 score68.8Unverified
9InternVL2-26B (SGP, token ratio 64%)GPT-4 score65.6Unverified
10Baichuan-Omni (7B)GPT-4 score65.4Unverified