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 726–750 of 2167 papers

TitleStatusHype
Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining—0
Interpretable Visual Question Answering by Reasoning on Dependency Trees—0
Interpretable Visual Question Answering via Reasoning Supervision—0
Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision—0
Crossformer: Transformer with Alternated Cross-Layer Guidance—0
Cross-Dataset Adaptation for Visual Question Answering—0
A Unified Framework for Multilingual and Code-Mixed Visual Question Answering—0
Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models—0
CQ-VQA: Visual Question Answering on Categorized Questions—0
Augmenting Image Question Answering Dataset by Exploiting Image Captions—0
CP-LLM: Context and Pixel Aware Large Language Model for Video Quality Assessment—0
Co-VQA : Answering by Interactive Sub Question Sequence—0
``Look, some Green Circles!'': Learning to Quantify from Images—0
Interpretable Visual Question Answering Referring to Outside Knowledge—0
Co-VQA : Answering by Interactive Sub Question Sequence—0
Audio-Visual Quality Assessment for User Generated Content: Database and Method—0
Accounting for Focus Ambiguity in Visual Questions—0
Counterfactual Vision and Language Learning—0
All You May Need for VQA are Image Captions—0
Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models—0
Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)—0
Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits—0
Attentive Explanations: Justifying Decisions and Pointing to the Evidence—0
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning—0
CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG—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