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Visual Question Answering

MLLM Leaderboard

Papers

Showing 501525 of 2177 papers

TitleStatusHype
Emu3: Next-Token Prediction is All You NeedCode3
Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations0
Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoECode1
Robotic Environmental State Recognition with Pre-Trained Vision-Language Models and Black-Box Optimization0
DARE: Diverse Visual Question Answering with Robustness Evaluation0
ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue0
A Unified Hallucination Mitigation Framework for Large Vision-Language ModelsCode0
MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation modelsCode1
Phantom of Latent for Large Language and Vision ModelsCode2
Detect, Describe, Discriminate: Moving Beyond VQA for MLLM Evaluation0
Can CLIP Count Stars? An Empirical Study on Quantity Bias in CLIP0
@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology0
Evaluating Image Hallucination in Text-to-Image Generation with Question-AnsweringCode1
Vision Language Models Can Parse Floor Plan Maps0
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any ResolutionCode11
Sparks of Artificial General Intelligence(AGI) in Semiconductor Material Science: Early Explorations into the Next Frontier of Generative AI-Assisted Electron Micrograph Analysis0
Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMsCode1
OneEncoder: A Lightweight Framework for Progressive Alignment of Modalities0
CAST: Cross-modal Alignment Similarity Test for Vision Language ModelsCode0
NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training0
Explore the Hallucination on Low-level Perception for MLLMs0
One missing piece in Vision and Language: A Survey on Comics UnderstandingCode2
Guiding Vision-Language Model Selection for Visual Question-Answering Across Tasks, Domains, and Knowledge TypesCode0
Learning to Compress Contexts for Efficient Knowledge-based Visual Question Answering0
Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial AttacksCode0
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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