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 1501–1525 of 2167 papers

TitleStatusHype
From Shallow to Deep: Compositional Reasoning over Graphs for Visual Question Answering—0
From Strings to Things: Knowledge-Enabled VQA Model That Can Read and Reason—0
CRIC: A VQA Dataset for Compositional Reasoning on Vision and Commonsense—0
From Wrong To Right: A Recursive Approach Towards Vision-Language Explanation—0
Full-reference Video Quality Assessment for User Generated Content Transcoding—0
FunBench: Benchmarking Fundus Reading Skills of MLLMs—0
Fusion of Detected Objects in Text for Visual Question Answering—0
Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering—0
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering—0
FVQA: Fact-based Visual Question Answering—0
GC-KBVQA: A New Four-Stage Framework for Enhancing Knowledge Based Visual Question Answering Performance—0
GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis—0
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning—0
Gemini Pro Defeated by GPT-4V: Evidence from Education—0
Gender and Racial Bias in Visual Question Answering Datasets—0
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems—0
Generalized Hadamard-Product Fusion Operators for Visual Question Answering—0
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge—0
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention—0
Generating Natural Questions from Images for Multimodal Assistants—0
Generating Rationales in Visual Question Answering—0
Generating Triples with Adversarial Networks for Scene Graph Construction—0
Generative Visual Question Answering—0
Geometry-Aware Video Quality Assessment for Dynamic Digital Human—0
GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing—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
9InternVL-CAccuracy81.2—Unverified
10LyricsAccuracy81.2—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