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

Showing 19011950 of 2167 papers

TitleStatusHype
PitVQA++: Vector Matrix-Low-Rank Adaptation for Open-Ended Visual Question Answering in Pituitary SurgeryCode0
Knowledge Guided Semi-Supervised Learning for Quality Assessment of User Generated VideosCode0
Knowledge Generation for Zero-shot Knowledge-based VQACode0
Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question AnsweringCode0
What Can Neural Networks Reason About?Code0
Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero TrainingCode0
Knowing Earlier what Right Means to You: A Comprehensive VQA Dataset for Grounding Relative Directions via Multi-Task LearningCode0
'Just because you are right, doesn't mean I am wrong': Overcoming a Bottleneck in the Development and Evaluation of Open-Ended Visual Question Answering (VQA) TasksCode0
JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated ImagesCode0
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning ModelCode0
Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language ModelsCode0
Pragmatic Issue-Sensitive Image CaptioningCode0
Temporal Reasoning via Audio Question AnsweringCode0
Joint Answering and Explanation for Visual Commonsense ReasoningCode0
Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-SupervisionCode0
Is Multimodal Vision Supervision Beneficial to Language?Code0
Self-Bootstrapped Visual-Language Model for Knowledge Selection and Question AnsweringCode0
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship DetectionCode0
Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question AnsweringCode0
Pretraining Vision-Language Model for Difference Visual Question Answering in Longitudinal Chest X-raysCode0
Diffusion-Refined VQA Annotations for Semi-Supervised Gaze FollowingCode0
IQ-VQA: Intelligent Visual Question AnsweringCode0
Difficult Task Yes but Simple Task No: Unveiling the Laziness in Multimodal LLMsCode0
Blind VQA on 360° Video via Progressively Learning from Pixels, Frames and VideoCode0
Blind Prediction of Natural Video QualityCode0
Differential Attention for Visual Question AnsweringCode0
IQA: Visual Question Answering in Interactive EnvironmentsCode0
A Dual-Attention Learning Network with Word and Sentence Embedding for Medical Visual Question AnsweringCode0
Visual Robustness Benchmark for Visual Question Answering (VQA)Code0
HumaniBench: A Human-Centric Framework for Large Multimodal Models EvaluationCode0
TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question AnsweringCode0
iParaphrasing: Extracting Visually Grounded Paraphrases via an ImageCode0
What is Right for Me is Not Yet Right for You: A Dataset for Grounding Relative Directions via Multi-Task LearningCode0
Biomedical Visual Instruction Tuning with Clinician Preference AlignmentCode0
Intrinsic Subgraph Generation for Interpretable Graph based Visual Question AnsweringCode0
Progressive Prompt Detailing for Improved Alignment in Text-to-Image Generative ModelsCode0
What is the Visual Cognition Gap between Humans and Multimodal LLMs?Code0
ViTextVQA: A Large-Scale Visual Question Answering Dataset for Evaluating Vietnamese Text Comprehension in ImagesCode0
BioD2C: A Dual-level Semantic Consistency Constraint Framework for Biomedical VQACode0
Interpretable Visual Reasoning via Induced Symbolic SpaceCode0
Viewport Proposal CNN for 360deg Video Quality AssessmentCode0
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and CompositionCode0
VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language ModelsCode0
BinaryVQA: A Versatile Test Set to Evaluate the Out-of-Distribution Generalization of VQA ModelsCode0
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural SupervisionCode0
Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question AnsweringCode0
The Promise of Premise: Harnessing Question Premises in Visual Question AnsweringCode0
Proximity QA: Unleashing the Power of Multi-Modal Large Language Models for Spatial Proximity AnalysisCode0
Are Red Roses Red? Evaluating Consistency of Question-Answering ModelsCode0
Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1humanAccuracy89.3Unverified
2DREAM+Unicoder-VL (MSRA)Accuracy76.04Unverified
3TRRNet (Ensemble)Accuracy74.03Unverified
4MIL-nbgaoAccuracy73.81Unverified
5Kakao BrainAccuracy73.33Unverified
6Coarse-to-Fine Reasoning, Single ModelAccuracy72.14Unverified
7270Accuracy70.23Unverified
8NSM ensemble (updated)Accuracy67.55Unverified
9VinVL-DPTAccuracy64.92Unverified
10VinVL+LAccuracy64.85Unverified
#ModelMetricClaimedVerifiedStatus
1PaLIAccuracy84.3Unverified
2BEiT-3Accuracy84.19Unverified
3VLMoAccuracy82.78Unverified
4ONE-PEACEAccuracy82.6Unverified
5mPLUG (Huge)Accuracy82.43Unverified
6CuMo-7BAccuracy82.2Unverified
7X2-VLM (large)Accuracy81.9Unverified
8MMUAccuracy81.26Unverified
9InternVL-CAccuracy81.2Unverified
10LyricsAccuracy81.2Unverified
#ModelMetricClaimedVerifiedStatus
1BEiT-3overall84.03Unverified
2mPLUG-Hugeoverall83.62Unverified
3ONE-PEACEoverall82.52Unverified
4X2-VLM (large)overall81.8Unverified
5VLMooverall81.3Unverified
6SimVLMoverall80.34Unverified
7X2-VLM (base)overall80.2Unverified
8VASToverall80.19Unverified
9VALORoverall78.62Unverified
10Prompt Tuningoverall78.53Unverified