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 1001–1025 of 2167 papers

TitleStatusHype
COCO is "ALL'' You Need for Visual Instruction Fine-tuning—0
Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources—0
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning—0
Do Explanations make VQA Models more Predictable to a Human?—0
Learning to Disambiguate by Asking Discriminative Questions—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
Asking questions on handwritten document collections—0
CoBIT: A Contrastive Bi-directional Image-Text Generation Model—0
Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios—0
Measuring Machine Intelligence Through Visual Question Answering—0
Learning to Recognize the Unseen Visual Predicates—0
FVQA: Fact-based Visual Question Answering—0
Learning to Specialize with Knowledge Distillation for Visual Question Answering—0
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering—0
Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model—0
Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering—0
Fusion of Detected Objects in Text for Visual Question Answering—0
LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?—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
Measuring CLEVRness: Blackbox testing of Visual Reasoning Models—0
Full-reference Video Quality Assessment for User Generated Content Transcoding—0
From Wrong To Right: A Recursive Approach Towards Vision-Language Explanation—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