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 901950 of 2167 papers

TitleStatusHype
Surgical-VQLA: Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic SurgeryCode1
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense CaptionerCode1
Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literature0
ONE-PEACE: Exploring One General Representation Model Toward Unlimited ModalitiesCode3
MedBLIP: Bootstrapping Language-Image Pre-training from 3D Medical Images and TextsCode1
An Empirical Study on the Language Modal in Visual Question Answering0
PMC-VQA: Visual Instruction Tuning for Medical Visual Question AnsweringCode1
TG-VQA: Ternary Game of Video Question Answering0
A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning0
Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video EnhancementCode1
SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement0
OCRBench: On the Hidden Mystery of OCR in Large Multimodal ModelsCode2
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction TuningCode2
Combo of Thinking and Observing for Outside-Knowledge VQACode1
OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in VietnameseCode0
Adaptive loose optimization for robust question answeringCode0
Otter: A Multi-Modal Model with In-Context Instruction TuningCode4
Analysis of Visual Question Answering Algorithms with attention model0
GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming ContentCode0
Visual Reasoning: from State to TransformationCode1
An Empirical Study of Multimodal Model MergingCode1
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction ModelCode5
Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality AssessmentCode1
An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients0
mPLUG-Owl: Modularization Empowers Large Language Models with MultimodalityCode4
A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question AnsweringCode1
Making Video Quality Assessment Models Robust to Bit Depth0
HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos0
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language ModelsCode7
SurgicalGPT: End-to-End Language-Vision GPT for Visual Question Answering in SurgeryCode1
Learning Situation Hyper-Graphs for Video Question AnsweringCode1
VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and DatasetCode2
Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective MethodCode1
PDFVQA: A New Dataset for Real-World VQA on PDF Documents0
Zoom-VQA: Patches, Frames and Clips Integration for Video Quality AssessmentCode1
CAVL: Learning Contrastive and Adaptive Representations of Vision and Language0
Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions0
Q2ATransformer: Improving Medical VQA via an Answer Querying Decoder0
Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA0
SC-ML: Self-supervised Counterfactual Metric Learning for Debiased Visual Question Answering0
Instance-Level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space0
MaMMUT: A Simple Architecture for Joint Learning for MultiModal TasksCode0
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init AttentionCode5
Unmasked Teacher: Towards Training-Efficient Video Foundation ModelsCode0
Curriculum Learning for Compositional Visual Reasoning0
MD-VQA: Multi-Dimensional Quality Assessment for UGC Live VideosCode1
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation LearningCode1
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation ModelsCode1
Top-Down Visual Attention from Analysis by SynthesisCode1
CoBIT: A Contrastive Bi-directional Image-Text Generation Model0
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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
9LyricsAccuracy81.2Unverified
10InternVL-CAccuracy81.2Unverified
#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