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 501550 of 2167 papers

TitleStatusHype
Meta-Learning via Classifier(-free) Diffusion GuidanceCode1
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation ModelsCode1
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language TasksCode1
Multi-Modal Answer Validation for Knowledge-Based VQACode1
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual ConceptsCode1
Multi-modal Auto-regressive Modeling via Visual WordsCode1
MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question AnsweringCode1
Fast Prompt Alignment for Text-to-Image GenerationCode1
Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question AnsweringCode1
Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4VCode1
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connectionsCode1
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question AnsweringCode1
Explaining Autonomous Driving Actions with Visual Question AnsweringCode1
FAVER: Blind Quality Prediction of Variable Frame Rate VideosCode1
Expectation-Maximization Contrastive Learning for Compact Video-and-Language RepresentationsCode1
Dynamic Language Binding in Relational Visual ReasoningCode1
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly DetectionCode1
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide ImagesCode1
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document UnderstandingCode1
eP-ALM: Efficient Perceptual Augmentation of Language ModelsCode1
MMUnlearner: Reformulating Multimodal Machine Unlearning in the Era of Multimodal Large Language ModelsCode1
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense CaptionerCode1
Enhancing Visual Question Answering through Question-Driven Image Captions as PromptsCode1
MM-Skin: Enhancing Dermatology Vision-Language Model with an Image-Text Dataset Derived from TextbooksCode1
Modular Visual Question Answering via Code GenerationCode1
MMFT-BERT: Multimodal Fusion Transformer with BERT Encodings for Visual Question AnsweringCode1
Are Bias Mitigation Techniques for Deep Learning Effective?Code1
MMNeuron: Discovering Neuron-Level Domain-Specific Interpretation in Multimodal Large Language ModelCode1
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question AnsweringCode1
Evaluating Multimodal Representations on Visual Semantic Textual SimilarityCode1
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question AnsweringCode1
Break It Down: A Question Understanding BenchmarkCode1
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question AnsweringCode1
MLP Architectures for Vision-and-Language Modeling: An Empirical StudyCode1
MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question AnsweringCode1
MMBERT: Multimodal BERT Pretraining for Improved Medical VQACode1
Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity CriterionCode1
Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency TrainingCode1
Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQACode1
Faithful Multimodal Explanation for Visual Question AnsweringCode1
Dual-Key Multimodal Backdoors for Visual Question AnsweringCode1
FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMsCode1
Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical AlignmentCode1
EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray ImagesCode1
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene UnderstandingCode1
FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis AssistantCode1
FiLM: Visual Reasoning with a General Conditioning LayerCode1
End-to-end Knowledge Retrieval with Multi-modal QueriesCode1
HIDRO-VQA: High Dynamic Range Oracle for Video Quality AssessmentCode1
End-to-end Document Recognition and Understanding with DessurtCode1
Show:102550
← PrevPage 11 of 44Next →

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