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 2026–2050 of 2167 papers

TitleStatusHype
STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering—0
Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering—0
StructuralLM: Structural Pre-training for Form Understanding—0
Structured Two-stream Attention Network for Video Question Answering—0
Structure Learning for Neural Module Networks—0
Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos—0
Study of the effect of Sharpness on Blind Video Quality Assessment—0
Subjective and Objective Analysis of Streamed Gaming Videos—0
Subjective and Objective Quality Assessment of Rendered Human Avatar Videos in Virtual Reality—0
Subtleties in the trainability of quantum machine learning models—0
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation—0
Supervising the Transfer of Reasoning Patterns in VQA—0
Surgical-LVLM: Learning to Adapt Large Vision-Language Model for Grounded Visual Question Answering in Robotic Surgery—0
SurgicalVLM-Agent: Towards an Interactive AI Co-Pilot for Pituitary Surgery—0
Survey of Recent Advances in Visual Question Answering—0
Survey of Visual Question Answering: Datasets and Techniques—0
Survey of Visual-Semantic Embedding Methods for Zero-Shot Image Retrieval—0
Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework—0
Syntax Tree Constrained Graph Network for Visual Question Answering—0
Synthesize Step-by-Step: Tools, Templates and LLMs as Data Generators for Reasoning-Based Chart VQA—0
Synthesize Step-by-Step: Tools Templates and LLMs as Data Generators for Reasoning-Based Chart VQA—0
T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts—0
Tackling VQA with Pretrained Foundation Models without Further Training—0
Take A Step Back: Rethinking the Two Stages in Visual Reasoning—0
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded—0
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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