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 9511000 of 2167 papers

TitleStatusHype
Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question AnsweringCode0
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question AnsweringCode1
eP-ALM: Efficient Perceptual Augmentation of Language ModelsCode1
3D Concept Learning and Reasoning from Multi-View Images0
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering0
Logical Implications for Visual Question Answering ConsistencyCode0
VDPVE: VQA Dataset for Perceptual Video EnhancementCode1
GPT-4 Technical ReportCode6
MRET: Multi-resolution Transformer for Video Quality Assessment0
Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region0
Vision-Language Models as Success Detectors0
PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical DocumentsCode2
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images0
MuLTI: Efficient Video-and-Language Understanding with Text-Guided MultiWay-Sampler and Multiple Choice Modeling0
Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language ModelsCode1
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning0
Toward Unsupervised Realistic Visual Question Answering0
Interpretable Visual Question Answering Referring to Outside Knowledge0
Graph Neural Networks in Vision-Language Image Understanding: A Survey0
PaLM-E: An Embodied Multimodal Language ModelCode2
Knowledge-Based Counterfactual Queries for Visual Question Answering0
VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media ReasoningCode0
Audio-Visual Quality Assessment for User Generated Content: Database and Method0
Prismer: A Vision-Language Model with Multi-Task ExpertsCode1
Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question AnsweringCode2
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of PneumothoraxCode1
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairsCode1
MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question AnsweringCode1
RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-trainingCode1
VQA with Cascade of Self- and Co-Attention Blocks0
Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity CriterionCode1
Medical visual question answering using joint self-supervised learning0
EVJVQA Challenge: Multilingual Visual Question Answering0
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?Code1
VinVL+L: Enriching Visual Representation with Location Context in VQACode0
Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia EntitiesCode1
Few-shot Multimodal Multitask Multilingual Learning0
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning0
Bridge Damage Cause Estimation Using Multiple Images Based on Visual Question Answering0
Multimodal Federated Learning via Contrastive Representation EnsembleCode1
Towards Unifying Medical Vision-and-Language Pre-training via Soft PromptsCode1
UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal ModelingCode1
Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
Is Multimodal Vision Supervision Beneficial to Language?Code0
Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its ApplicationsCode1
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and VideoCode4
UPop: Unified and Progressive Pruning for Compressing Vision-Language TransformersCode1
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language ModelsCode4
BinaryVQA: A Versatile Test Set to Evaluate the Out-of-Distribution Generalization of VQA ModelsCode0
Towards a Unified Model for Generating Answers and Explanations in Visual Question Answering0
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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