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 801850 of 2167 papers

TitleStatusHype
Neural Module NetworksCode0
HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot InteractionCode0
Fully Authentic Visual Question Answering Dataset from Online CommunitiesCode0
https://arxiv.org/abs/2407.00634Code0
Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question AnsweringCode0
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A PlatformsCode0
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and ResultsCode0
A simple neural network module for relational reasoningCode0
Multimodal Residual Learning for Visual QACode0
A Simple Loss Function for Improving the Convergence and Accuracy of Visual Question Answering ModelsCode0
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question AnsweringCode0
CLIPVQA:Video Quality Assessment via CLIPCode0
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question AnsweringCode0
A Simple Baseline for Knowledge-Based Visual Question AnsweringCode0
From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language ModelsCode0
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual GroundingCode0
ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question AnsweringCode0
Multimodal Explanations: Justifying Decisions and Pointing to the EvidenceCode0
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language UnderstandingCode0
Multi-Image Visual Question AnsweringCode0
II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question AnsweringCode0
ILLUME: Rationalizing Vision-Language Models through Human InteractionsCode0
FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question AnsweringCode0
ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical ImagesCode0
MQA: Answering the Question via Robotic ManipulationCode0
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring ExpressionsCode0
Focal Visual-Text Attention for Visual Question AnsweringCode0
Focal Visual-Text Attention for Memex Question AnsweringCode0
CLEVR\_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over ImagesCode0
CLEVR_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over ImagesCode0
ArtQuest: Countering Hidden Language Biases in ArtVQACode0
Modeling Relationships in Referential Expressions with Compositional Modular NetworksCode0
HumaniBench: A Human-Centric Framework for Large Multimodal Models EvaluationCode0
CLEAR: A Dataset for Compositional Language and Elementary Acoustic ReasoningCode0
Modularized Zero-shot VQA with Pre-trained ModelsCode0
Modulating early visual processing by languageCode0
Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic ApproachCode0
Revisiting Video Quality Assessment from the Perspective of GeneralizationCode0
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask QuestionsCode0
Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question AnsweringCode0
FigureQA: An Annotated Figure Dataset for Visual ReasoningCode0
Improved RAMEN: Towards Domain Generalization for Visual Question AnsweringCode0
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question AnsweringCode0
Few-Shot Multimodal Explanation for Visual Question AnsweringCode0
Federated Document Visual Question Answering: A Pilot StudyCode0
MIRTT: Learning Multimodal Interaction Representations from Trilinear Transformers for Visual Question AnsweringCode0
MHSAN: Multi-Head Self-Attention Network for Visual Semantic EmbeddingCode0
Factor Graph AttentionCode0
Med-PMC: Medical Personalized Multi-modal Consultation with a Proactive Ask-First-Observe-Next ParadigmCode0
Medical Large Vision Language Models with Multi-Image Visual AbilityCode0
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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