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 1401–1425 of 2167 papers

TitleStatusHype
Document Visual Question Answering Challenge 2020—0
Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think!—0
Do Explanations make VQA Models more Predictable to a Human?—0
Domain-robust VQA with diverse datasets and methods but no target labels—0
DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment—0
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions—0
Dual Capsule Attention Mask Network with Mutual Learning for Visual Question Answering—0
DualNet: Domain-Invariant Network for Visual Question Answering—0
DUBLIN -- Document Understanding By Language-Image Network—0
DVLTA-VQA: Decoupled Vision-Language Modeling with Text-Guided Adaptation for Blind Video Quality Assessment—0
Dynamic Fusion with Intra- and Inter- Modality Attention Flow for Visual Question Answering—0
Dynamic Fusion With Intra- and Inter-Modality Attention Flow for Visual Question Answering—0
Dynamic Inference With Grounding Based Vision and Language Models—0
DynRsl-VLM: Enhancing Autonomous Driving Perception with Dynamic Resolution Vision-Language Models—0
eaVQA: An Experimental Analysis on Visual Question Answering Models—0
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering—0
EchoSight: Advancing Visual-Language Models with Wiki Knowledge—0
Edit me: A Corpus and a Framework for Understanding Natural Language Image Editing—0
Efficient Bilinear Attention-based Fusion for Medical Visual Question Answering—0
Efficient Few-Shot Continual Learning in Vision-Language Models—0
Efficient Quantum Gradient and Higher-order Derivative Estimation via Generalized Hadamard Test—0
ElectroVizQA: How well do Multi-modal LLMs perform in Electronics Visual Question Answering?—0
Eliminating Catastrophic Interference with Biased Competition—0
Eliminating the Language Bias for Visual Question Answering with fine-grained Causal Intervention—0
Embodied Scene Understanding for Vision Language Models via MetaVQA—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