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 1101–1125 of 2167 papers

TitleStatusHype
VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization—0
VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering—0
VQA-LOL: Visual Question Answering under the Lens of Logic—0
VQA-MHUG: A Gaze Dataset to Study Multimodal Neural Attention in Visual Question Answering—0
VQA Training Sets are Self-play Environments for Generating Few-shot Pools—0
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models—0
VQA with Cascade of Self- and Co-Attention Blocks—0
VSA4VQA: Scaling a Vector Symbolic Architecture to Visual Question Answering on Natural Images—0
Watching the News: Towards VideoQA Models that can Read—0
Weakly Supervised Visual Question Answer Generation—0
Weak Supervision helps Emergence of Word-Object Alignment and improves Vision-Language Tasks—0
Webly Supervised Concept Expansion for General Purpose Vision Models—0
What is needed for simple spatial language capabilities in VQA?—0
What Large Language Models Bring to Text-rich VQA?—0
What makes a good metric? Evaluating automatic metrics for text-to-image consistency—0
When are Lemons Purple? The Concept Association Bias of Vision-Language Models—0
Where is this coming from? Making groundedness count in the evaluation of Document VQA models—0
Where To Look: Focus Regions for Visual Question Answering—0
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering—0
Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities—0
Why Does a Visual Question Have Different Answers?—0
Why Does the VQA Model Answer No?: Improving Reasoning through Visual and Linguistic Inference—0
WoLF: Wide-scope Large Language Model Framework for CXR Understanding—0
Workshop on Document Intelligence Understanding—0
WSI-LLaVA: A Multimodal Large Language Model for Whole Slide Image—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