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 976–1000 of 2167 papers

TitleStatusHype
LEAF-QA: Locate, Encode & Attend for Figure Question Answering—0
GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis—0
GC-KBVQA: A New Four-Stage Framework for Enhancing Knowledge Based Visual Question Answering Performance—0
Making Video Quality Assessment Models Robust to Bit Depth—0
Learning by Asking Questions—0
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision—0
Learning Compositional Representation for Few-shot Visual Question Answering—0
Asking questions on handwritten document collections—0
CoBIT: A Contrastive Bi-directional Image-Text Generation Model—0
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations—0
FVQA: Fact-based Visual Question Answering—0
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering—0
Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering—0
Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues—0
Document AI: Benchmarks, Models and Applications—0
Making the V in Text-VQA Matter—0
Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering—0
Learning Sparse Mixture of Experts for Visual Question Answering—0
Learning to Answer Multilingual and Code-Mixed Questions—0
Fusion of Detected Objects in Text for Visual Question Answering—0
Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment—0
FunBench: Benchmarking Fundus Reading Skills of MLLMs—0
Asking More Informative Questions for Grounded Retrieval—0
MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering—0
Making Video Quality Assessment Models Sensitive to Frame Rate Distortions—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