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 951–1000 of 2167 papers

TitleStatusHype
Multimodal Explanations: Justifying Decisions and Pointing to the EvidenceCode0
MHSAN: Multi-Head Self-Attention Network for Visual Semantic EmbeddingCode0
Enhancing Vietnamese VQA through Curriculum Learning on Raw and Augmented Text RepresentationsCode0
Enhancing the AI2 Diagrams Dataset Using Rhetorical Structure TheoryCode0
Medical Large Vision Language Models with Multi-Image Visual AbilityCode0
Med-PMC: Medical Personalized Multi-modal Consultation with a Proactive Ask-First-Observe-Next ParadigmCode0
Mimic and Fool: A Task Agnostic Adversarial AttackCode0
Measuring Faithful and Plausible Visual Grounding in VQACode0
Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature DistillationCode0
μ-Bench: A Vision-Language Benchmark for Microscopy UnderstandingCode0
Answer Them All! Toward Universal Visual Question Answering ModelsCode0
MedHallTune: An Instruction-Tuning Benchmark for Mitigating Medical Hallucination in Vision-Language ModelsCode0
End-to-end optimization of goal-driven and visually grounded dialogue systemsCode0
End-to-End Instance Segmentation with Recurrent AttentionCode0
Answer Questions with Right Image Regions: A Visual Attention Regularization ApproachCode0
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video FeaturesCode0
Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question AnsweringCode0
MaMMUT: A Simple Architecture for Joint Learning for MultiModal TasksCode0
M^2ConceptBase: A Fine-Grained Aligned Concept-Centric Multimodal Knowledge BaseCode0
Visual Contexts Clarify Ambiguous Expressions: A Benchmark DatasetCode0
ELIP: Efficient Language-Image Pre-training with Fewer Vision TokensCode0
Answering Questions about Data Visualizations using Efficient Bimodal FusionCode0
LXMERT Model Compression for Visual Question AnsweringCode0
LayoutLMv3: Pre-training for Document AI with Unified Text and Image MaskingCode0
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question AnsweringCode0
Marten: Visual Question Answering with Mask Generation for Multi-modal Document UnderstandingCode0
Logical Implications for Visual Question Answering ConsistencyCode0
Locally Smoothed Neural NetworksCode0
Effective Approaches to Batch Parallelization for Dynamic Neural Network ArchitecturesCode0
Looking Beyond Visible Cues: Implicit Video Question Answering via Dual-Clue ReasoningCode0
Visuo-Linguistic Question Answering (VLQA) ChallengeCode0
Visual Question Answering: A Survey of Methods and DatasetsCode0
LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic SurgeryCode0
Learning content and context with language bias for Visual Question AnsweringCode0
Learning Convolutional Text Representations for Visual Question AnsweringCode0
Bridging Languages through Images with Deep Partial Canonical Correlation AnalysisCode0
ECG Heartbeat Classification: A Deep Transferable RepresentationCode0
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning ModelCode0
BioD2C: A Dual-level Semantic Consistency Constraint Framework for Biomedical VQACode0
Learning Representations of Sets through Optimized PermutationsCode0
LMM-VQA: Advancing Video Quality Assessment with Large Multimodal ModelsCode0
Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance ViewCode0
EaSe: A Diagnostic Tool for VQA based on Answer DiversityCode0
Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal ImageryCode0
LININ: Logic Integrated Neural Inference Network for Explanatory Visual Question AnsweringCode0
Dynamic Memory Networks for Visual and Textual Question AnsweringCode0
Targeted Visual Prompting for Medical Visual Question AnsweringCode0
LLaVA-OneVision: Easy Visual Task TransferCode0
Learning to Collocate Visual-Linguistic Neural Modules for Image CaptioningCode0
LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question AnsweringCode0
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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