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 1351–1375 of 2167 papers

TitleStatusHype
CQ-VQA: Visual Question Answering on Categorized Questions—0
Cross-Dataset Adaptation for Visual Question Answering—0
Crossformer: Transformer with Alternated Cross-Layer Guidance—0
Cross-Modal Generative Augmentation for Visual Question Answering—0
Cross-modal Knowledge Reasoning for Knowledge-based Visual Question Answering—0
Cross-Modal Retrieval Augmentation for Multi-Modal Classification—0
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization—0
CS-VQA: Visual Question Answering with Compressively Sensed Images—0
CT-Agent: A Multimodal-LLM Agent for 3D CT Radiology Question Answering—0
Curriculum Learning Effectively Improves Low Data VQA—0
Curriculum Learning for Compositional Visual Reasoning—0
Curriculum reinforcement learning for quantum architecture search under hardware errors—0
Curriculum Script Distillation for Multilingual Visual Question Answering—0
C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset—0
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark—0
Cycle-Consistency for Robust Visual Question Answering—0
DARE: Diverse Visual Question Answering with Robustness Evaluation—0
Data Augmentation for Visual Question Answering—0
Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction—0
PlotQA: Reasoning over Scientific Plots—0
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation—0
DCVQE: A Hierarchical Transformer for Video Quality Assessment—0
DDRprog: A CLEVR Differentiable Dynamic Reasoning Programmer—0
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation—0
Debating for Better Reasoning: An Unsupervised Multimodal Approach—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