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 276–300 of 2167 papers

TitleStatusHype
IMPACT: A Large-scale Integrated Multimodal Patent Analysis and Creation Dataset for Design PatentsCode1
MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference OptimizationCode2
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling—0
MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at ScaleCode1
Verb Mirage: Unveiling and Assessing Verb Concept Hallucinations in Multimodal Large Language Models—0
Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth FusionCode3
T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts—0
Video Quality Assessment: A Comprehensive SurveyCode2
AdvDreamer Unveils: Are Vision-Language Models Truly Ready for Real-World 3D Variations?—0
WSI-LLaVA: A Multimodal Large Language Model for Whole Slide Image—0
Copy-Move Forgery Detection and Question Answering for Remote Sensing ImageCode0
CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMs—0
DLaVA: Document Language and Vision Assistant for Answer Localization with Enhanced Interpretability and TrustworthinessCode0
SURE-VQA: Systematic Understanding of Robustness Evaluation in Medical VQA TasksCode0
Perception Test 2024: Challenge Summary and a Novel Hour-Long VideoQA Benchmark—0
Sparse Attention Vectors: Generative Multimodal Model Features Are Discriminative Vision-Language Classifiers—0
ElectroVizQA: How well do Multi-modal LLMs perform in Electronics Visual Question Answering?—0
Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question AnsweringCode2
Grounding-IQA: Multimodal Language Grounding Model for Image Quality AssessmentCode2
AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMMCode1
Task Progressive Curriculum Learning for Robust Visual Question Answering—0
Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey—0
GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis—0
Video-Text Dataset Construction from Multi-AI Feedback: Promoting Weak-to-Strong Preference Learning for Video Large Language Models—0
ZoomEye: Enhancing Multimodal LLMs with Human-Like Zooming Capabilities through Tree-Based Image ExplorationCode2
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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
9InternVL-CAccuracy81.2—Unverified
10LyricsAccuracy81.2—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