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 1301–1325 of 2167 papers

TitleStatusHype
Proposal-free One-stage Referring Expression via Grid-Word Cross-Attention—0
Proposing Plausible Answers for Open-ended Visual Question Answering—0
Provoking Multi-modal Few-Shot LVLM via Exploration-Exploitation In-Context Learning—0
Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting—0
Psycholinguistics meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering—0
PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild—0
Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information—0
PuzzleBench: A Fully Dynamic Evaluation Framework for Large Multimodal Models on Puzzle Solving—0
Pyramid Coder: Hierarchical Code Generator for Compositional Visual Question Answering—0
Q2ATransformer: Improving Medical VQA via an Answer Querying Decoder—0
Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Models—0
QIRL: Boosting Visual Question Answering via Optimized Question-Image Relation Learning—0
QSAN: A Near-term Achievable Quantum Self-Attention Network—0
QTG-VQA: Question-Type-Guided Architectural for VideoQA Systems—0
Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities—0
Question-Agnostic Attention for Visual Question Answering—0
Question-Conditioned Counterfactual Image Generation for VQA—0
Question-Driven Graph Fusion Network For Visual Question Answering—0
Question Generation for Evaluating Cross-Dataset Shifts in Multi-modal Grounding—0
Question-Guided Hybrid Convolution for Visual Question Answering—0
Question Guided Modular Routing Networks for Visual Question Answering—0
Question-Led Semantic Structure Enhanced Attentions for VQA—0
Question Modifiers in Visual Question Answering—0
Question Relevance in Visual Question Answering—0
Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions—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
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