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 1126–1150 of 2167 papers

TitleStatusHype
From Wrong To Right: A Recursive Approach Towards Vision-Language Explanation—0
KNVQA: A Benchmark for evaluation knowledge-based VQA—0
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask QuestionsCode0
Understanding and Mitigating Classification Errors Through Interpretable Token Patterns—0
Attribute Diversity Determines the Systematicity Gap in VQACode0
Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question PromptsCode0
Multiple-Question Multiple-Answer Text-VQA—0
Asking More Informative Questions for Grounded Retrieval—0
CLiF-VQA: Enhancing Video Quality Assessment by Incorporating High-Level Semantic Information related to Human Feelings—0
What Large Language Models Bring to Text-rich VQA?—0
Visual Commonsense based Heterogeneous Graph Contrastive Learning—0
Analyzing Modular Approaches for Visual Question DecompositionCode0
Improving Vision-and-Language Reasoning via Spatial Relations Modeling—0
Zero-shot Translation of Attention Patterns in VQA Models to Natural LanguageCode0
From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities—0
VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization—0
A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis—0
Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal ImageryCode0
ViCLEVR: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in VietnameseCode0
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation—0
Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering PairsCode0
Exploring Question Decomposition for Zero-Shot VQA—0
Geometry-Aware Video Quality Assessment for Dynamic Digital Human—0
LXMERT Model Compression for Visual Question AnsweringCode0
A Simple Baseline for Knowledge-Based Visual Question AnsweringCode0
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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