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 676–700 of 2167 papers

TitleStatusHype
Improving Generalization in Visual Reasoning via Self-Ensemble—0
Debating for Better Reasoning: An Unsupervised Multimodal Approach—0
Bayesian Attention Belief Networks—0
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation—0
DDRprog: A CLEVR Differentiable Dynamic Reasoning Programmer—0
BARTPhoBEiT: Pre-trained Sequence-to-Sequence and Image Transformers Models for Vietnamese Visual Question Answering—0
An Analysis of Visual Question Answering Algorithms—0
Improving Medical Reasoning with Curriculum-Aware Reinforcement Learning—0
DCVQE: A Hierarchical Transformer for Video Quality Assessment—0
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation—0
Improved Few-Shot Image Classification Through Multiple-Choice Questions—0
PlotQA: Reasoning over Scientific Plots—0
Improved Bilinear Pooling with CNNs—0
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection—0
Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction—0
BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs—0
Data Augmentation for Visual Question Answering—0
DARE: Diverse Visual Question Answering with Robustness Evaluation—0
Backdooring Vision-Language Models with Out-Of-Distribution Data—0
A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment—0
Improving Automatic VQA Evaluation Using Large Language Models—0
Improving mitosis detection on histopathology images using large vision-language models—0
Achieving Human Parity on Visual Question Answering—0
Analysis on Image Set Visual Question Answering—0
Image Semantic Relation Generation—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