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 1226–1250 of 2167 papers

TitleStatusHype
Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literature—0
An Empirical Study on the Language Modal in Visual Question Answering—0
TG-VQA: Ternary Game of Video Question Answering—0
A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning—0
SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement—0
OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in VietnameseCode0
Adaptive loose optimization for robust question answeringCode0
Analysis of Visual Question Answering Algorithms with attention model—0
GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming ContentCode0
An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients—0
HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos—0
Making Video Quality Assessment Models Robust to Bit Depth—0
PDFVQA: A New Dataset for Real-World VQA on PDF Documents—0
CAVL: Learning Contrastive and Adaptive Representations of Vision and Language—0
Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions—0
Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA—0
SC-ML: Self-supervised Counterfactual Metric Learning for Debiased Visual Question Answering—0
Q2ATransformer: Improving Medical VQA via an Answer Querying Decoder—0
Instance-Level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space—0
MaMMUT: A Simple Architecture for Joint Learning for MultiModal TasksCode0
Unmasked Teacher: Towards Training-Efficient Video Foundation ModelsCode0
Curriculum Learning for Compositional Visual Reasoning—0
CoBIT: A Contrastive Bi-directional Image-Text Generation Model—0
Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question AnsweringCode0
3D Concept Learning and Reasoning from Multi-View Images—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