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 1251–1275 of 2167 papers

TitleStatusHype
Attention Mechanism based Cognition-level Scene Understanding—0
Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!—0
Attentive Explanations: Justifying Decisions and Pointing to the Evidence—0
Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)—0
Audio-Visual Quality Assessment for User Generated Content: Database and Method—0
Augmenting Image Question Answering Dataset by Exploiting Image Captions—0
A Unified Framework for Multilingual and Code-Mixed Visual Question Answering—0
Auto-Parsing Network for Image Captioning and Visual Question Answering—0
AVIS: Autonomous Visual Information Seeking with Large Language Model Agent—0
A Vision Centric Remote Sensing Benchmark—0
A Visual Question Answering Method for SAR Ship: Breaking the Requirement for Multimodal Dataset Construction and Model Fine-Tuning—0
Avoiding Barren Plateaus with Classical Deep Neural Networks—0
Backdooring Vision-Language Models with Out-Of-Distribution Data—0
BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs—0
BARTPhoBEiT: Pre-trained Sequence-to-Sequence and Image Transformers Models for Vietnamese Visual Question Answering—0
Bayesian Attention Belief Networks—0
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets—0
@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology—0
Benchmarking Large Multimodal Models for Ophthalmic Visual Question Answering with OphthalWeChat—0
Benchmarking Multimodal Models for Ukrainian Language Understanding Across Academic and Cultural Domains—0
What BERT Sees: Cross-Modal Transfer for Visual Question Generation—0
BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answering—0
Beyond Captioning: Task-Specific Prompting for Improved VLM Performance in Mathematical Reasoning—0
Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis—0
Beyond the Hype: A dispassionate look at vision-language models in medical scenario—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