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 1376–1400 of 2167 papers

TitleStatusHype
Decouple Before Interact: Multi-Modal Prompt Learning for Continual Visual Question Answering—0
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering—0
Deep Attention Neural Tensor Network for Visual Question Answering—0
Deep Bayesian Active Learning for Multiple Correct Outputs—0
Deep Equilibrium Multimodal Fusion—0
Deep Exemplar Networks for VQA and VQG—0
Deep learning evaluation using deep linguistic processing—0
Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study—0
Deep Video Quality Assessor: From Spatio-temporal Visual Sensitivity to A Convolutional Neural Aggregation Network—0
RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training—0
DePlot: One-shot visual language reasoning by plot-to-table translation—0
Detect2Interact: Localizing Object Key Field in Visual Question Answering (VQA) with LLMs—0
Detect, Describe, Discriminate: Moving Beyond VQA for MLLM Evaluation—0
Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions—0
DIEM: Decomposition-Integration Enhancing Multimodal Insights—0
Differentiable End-to-End Program Executor for Sample and Computationally Efficient VQA—0
DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor—0
DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels—0
Directional Gradient Projection for Robust Fine-Tuning of Foundation Models—0
Disentangling Knowledge-based and Visual Reasoning by Question Decomposition in KB-VQA—0
Distraction-free Embeddings for Robust VQA—0
Diversity and Consistency: Exploring Visual Question-Answer Pair Generation—0
Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback—0
Document AI: Benchmarks, Models and Applications—0
Document Collection Visual Question Answering—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