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 1451–1475 of 2167 papers

TitleStatusHype
OG-SGG: Ontology-Guided Scene Graph Generation. A Case Study in Transfer Learning for Telepresence RoboticsCode0
RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training—0
Delving Deeper into Cross-lingual Visual Question AnsweringCode0
Privacy Preserving Visual Question Answering—0
An experimental study of the vision-bottleneck in VQA—0
Can Open Domain Question Answering Systems Answer Visual Knowledge Questions?—0
NEWSKVQA: Knowledge-Aware News Video Question Answering—0
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning FrameworkCode0
Grounding Answers for Visual Questions Asked by Visually Impaired PeopleCode0
Webly Supervised Concept Expansion for General Purpose Vision Models—0
Compositionality as Lexical SymmetryCode0
Transformer Module Networks for Systematic Generalization in Visual Question AnsweringCode0
Learning to Compose Diversified Prompts for Image Emotion Classification—0
MGA-VQA: Multi-Granularity Alignment for Visual Question Answering—0
SA-VQA: Structured Alignment of Visual and Semantic Representations for Visual Question Answering—0
Question Generation for Evaluating Cross-Dataset Shifts in Multi-modal Grounding—0
KAT: A Knowledge Augmented Transformer for Vision-and-Language—0
All You May Need for VQA are Image Captions—0
Task Formulation Matters When Learning Continuously: A Case Study in Visual Question Answering—0
Probing the Role of Positional Information in Vision-Language Models—0
Retrieving Visual Facts For Few-Shot Visual Question Answering—0
MANGO: Enhancing the Robustness of VQA Models via Adversarial Noise Generation—0
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks—0
A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering—0
Towards Automated Error Analysis: Learning to Characterize Errors—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