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 1551–1575 of 2167 papers

TitleStatusHype
UNITER: Learning UNiversal Image-TExt Representations—0
Un jeu de données pour répondre à des questions visuelles à propos d’entités nommées en utilisant des bases de connaissances (ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named Entities)—0
Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenario—0
Unshuffling Data for Improved Generalization—0
Unshuffling Data for Improved Generalization in Visual Question Answering—0
Unsupervised Keyword Extraction for Full-sentence VQA—0
Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment—0
Unveiling Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA—0
UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation—0
Using Visual Cropping to Enhance Fine-Detail Question Answering of BLIP-Family Models—0
V^2Dial: Unification of Video and Visual Dialog via Multimodal Experts—0
V^2Dial: Unification of Video and Visual Dialog via Multimodal Experts—0
VALSE: A Task-Independent Benchmark for Vision and Language Models centered on Linguistic Phenomena—0
Variational Disentangled Attention for Regularized Visual Dialog—0
Variational Visual Question Answering—0
V-Doc : Visual questions answers with Documents—0
V-Doc: Visual Questions Answers With Documents—0
Verb Mirage: Unveiling and Assessing Verb Concept Hallucinations in Multimodal Large Language Models—0
VGNMN: Video-grounded Neural Module Networks for Video-Grounded Dialogue Systems—0
VGNMN: Video-grounded Neural Module Network to Video-Grounded Language Tasks—0
Video Instruction Tuning With Synthetic Data—0
Video Quality Assessment Based on Swin TransformerV2 and Coarse to Fine Strategy—0
Video Quality Assessment for Online Processing: From Spatial to Temporal Sampling—0
Video Question Answering via Attribute-Augmented Attention Network Learning—0
Video Question Answering with Iterative Video-Text Co-Tokenization—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