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 1001–1025 of 2167 papers

TitleStatusHype
SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge—0
Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question AnsweringCode0
Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions—0
EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging—0
StackOverflowVQA: Stack Overflow Visual Question Answering Dataset—0
RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content—0
Realizing Visual Question Answering for Education: GPT-4V as a Multimodal AI—0
Federated Document Visual Question Answering: A Pilot StudyCode0
Is the House Ready For Sleeptime? Generating and Evaluating Situational Queries for Embodied Question Answering—0
Advancing Multimodal Medical Capabilities of Gemini—0
VSA4VQA: Scaling a Vector Symbolic Architecture to Visual Question Answering on Natural Images—0
Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis—0
Enhanced Textual Feature Extraction for Visual Question Answering: A Simple Convolutional Approach—0
Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation—0
Multi-Page Document Visual Question Answering using Self-Attention Scoring MechanismCode0
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge—0
RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT AnalysisCode0
Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering—0
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and ResultsCode0
Self-Bootstrapped Visual-Language Model for Knowledge Selection and Question AnsweringCode0
Exploring Diverse Methods in Visual Question Answering—0
PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering—0
TextSquare: Scaling up Text-Centric Visual Instruction Tuning—0
Look Before You Decide: Prompting Active Deduction of MLLMs for Assumptive Reasoning—0
Unified Scene Representation and Reconstruction for 3D Large Language Models—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