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 1201–1225 of 2167 papers

TitleStatusHype
An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients—0
An Empirical Evaluation of Visual Question Answering for Novel Objects—0
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation—0
An Empirical Study on Leveraging Scene Graphs for Visual Question Answering—0
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games—0
An Empirical Study on the Language Modal in Visual Question Answering—0
An Evaluation of GPT-4V and Gemini in Online VQA—0
An Evaluation of Image-Based Verb Prediction Models against Human Eye-Tracking Data—0
An experimental study of the vision-bottleneck in VQA—0
Annotation Methodologies for Vision and Language Dataset Creation—0
A Novel Attention-based Aggregation Function to Combine Vision and Language—0
A Novel Framework for Robustness Analysis of Visual QA Models—0
A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning—0
Answer-checking in Context: A Multi-modal FullyAttention Network for Visual Question Answering—0
Answer-Me: Multi-Task Open-Vocabulary Visual Question Answering—0
Answer-Type Prediction for Visual Question Answering—0
AOR: Anatomical Ontology-Guided Reasoning for Medical Large Multimodal Model in Chest X-Ray Interpretation—0
A Picture May Be Worth a Hundred Words for Visual Question Answering—0
Application of Multimodal Large Language Models in Autonomous Driving—0
ArcSin: Adaptive ranged cosine Similarity injected noise for Language-Driven Visual Tasks—0
A reinforcement learning approach for VQA validation: an application to diabetic macular edema grading—0
A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators—0
A Restricted Visual Turing Test for Deep Scene and Event Understanding—0
A review of Quantum Neural Networks: Methods, Models, Dilemma—0
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions—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