SOTAVerified

Visual Question Answering (VQA)

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

Papers

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Showing 376–400 of 2167 papers

TitleStatusHype
ActiView: Evaluating Active Perception Ability for Multimodal Large Language ModelsCode1
MC-CoT: A Modular Collaborative CoT Framework for Zero-shot Medical-VQA with LLM and MLLM IntegrationCode1
TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable QuestionsCode0
BadCM: Invisible Backdoor Attack Against Cross-Modal LearningCode1
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—0
Video Instruction Tuning With Synthetic Data—0
Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities—0
Backdooring Vision-Language Models with Out-Of-Distribution Data—0
Unleashing the Potentials of Likelihood Composition for Multi-modal Language ModelsCode0
BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured DataCode0
FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks—0
A Hitchhikers Guide to Fine-Grained Face Forgery Detection Using Common Sense ReasoningCode1
T2Vs Meet VLMs: A Scalable Multimodal Dataset for Visual Harmfulness RecognitionCode1
Visual Question Decomposition on Multimodal Large Language Models—0
TrojVLM: Backdoor Attack Against Vision Language Models—0
3D-CT-GPT: Generating 3D Radiology Reports through Integration of Large Vision-Language Models—0
Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations—0
DARE: Diverse Visual Question Answering with Robustness Evaluation—0
ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue—0
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
Advancing Video Quality Assessment for AIGC—0
Revisiting Video Quality Assessment from the Perspective of GeneralizationCode0
Detect, Describe, Discriminate: Moving Beyond VQA for MLLM Evaluation—0
Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language ModelsCode0
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All datasetsGQA Test2019VQA v2 test-devVQA v2 test-stdOK-VQAMSVD-QADocVQA testMSRVTT-QAInfographicVQAGQA test-devVizWiz 2020 VQAA-OKVQACLEVR

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1humanAccuracy89.3—Unverified
2DREAM+Unicoder-VL (MSRA)Accuracy76.04—Unverified
3TRRNet (Ensemble)Accuracy74.03—Unverified
4MIL-nbgaoAccuracy73.81—Unverified
5Kakao BrainAccuracy73.33—Unverified
6Coarse-to-Fine Reasoning, Single ModelAccuracy72.14—Unverified
7270Accuracy70.23—Unverified
8NSM ensemble (updated)Accuracy67.55—Unverified
9VinVL-DPTAccuracy64.92—Unverified
10VinVL+LAccuracy64.85—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLIAccuracy84.3—Unverified
2BEiT-3Accuracy84.19—Unverified
3VLMoAccuracy82.78—Unverified
4ONE-PEACEAccuracy82.6—Unverified
5mPLUG (Huge)Accuracy82.43—Unverified
6CuMo-7BAccuracy82.2—Unverified
7X2-VLM (large)Accuracy81.9—Unverified
8MMUAccuracy81.26—Unverified
9LyricsAccuracy81.2—Unverified
10InternVL-CAccuracy81.2—Unverified
#ModelMetricClaimedVerifiedStatus
1BEiT-3overall84.03—Unverified
2mPLUG-Hugeoverall83.62—Unverified
3ONE-PEACEoverall82.52—Unverified
4X2-VLM (large)overall81.8—Unverified
5VLMooverall81.3—Unverified
6SimVLMoverall80.34—Unverified
7X2-VLM (base)overall80.2—Unverified
8VASToverall80.19—Unverified
9VALORoverall78.62—Unverified
10Prompt Tuningoverall78.53—Unverified