SOTAVerified

Visual Question Answering

MLLM Leaderboard

Papers

Showing 451475 of 2177 papers

TitleStatusHype
Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and GenerationCode11
H2OVL-Mississippi Vision Language Models Technical Report0
γ-MoD: Exploring Mixture-of-Depth Adaptation for Multimodal Large Language Models0
WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global CuisinesCode1
Cross-Modal Safety Mechanism Transfer in Large Vision-Language Models0
VividMed: Vision Language Model with Versatile Visual Grounding for MedicineCode1
OMCAT: Omni Context Aware Transformer0
Difficult Task Yes but Simple Task No: Unveiling the Laziness in Multimodal LLMsCode0
MMFuser: Multimodal Multi-Layer Feature Fuser for Fine-Grained Vision-Language UnderstandingCode2
MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling0
Towards Foundation Models for 3D Vision: How Close Are We?Code1
Eliminating the Language Bias for Visual Question Answering with fine-grained Causal Intervention0
Surgical-LLaVA: Toward Surgical Scenario Understanding via Large Language and Vision Models0
MMCOMPOSITION: Revisiting the Compositionality of Pre-trained Vision-Language Models0
Declarative Knowledge Distillation from Large Language Models for Visual Question Answering DatasetsCode0
Zero-shot Commonsense Reasoning over Machine ImaginationCode0
Skipping Computations in Multimodal LLMsCode1
VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment0
Baichuan-Omni Technical ReportCode3
Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language BootstrappingCode1
ViT3D Alignment of LLaMA3: 3D Medical Image Report Generation0
VoxelPrompt: A Vision-Language Agent for Grounded Medical Image AnalysisCode2
Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training0
Emerging Pixel Grounding in Large Multimodal Models Without Grounding Supervision0
PAR: Prompt-Aware Token Reduction Method for Efficient Large Multimodal Models0
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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