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 15011550 of 2167 papers

TitleStatusHype
VisQA: X-raying Vision and Language Reasoning in TransformersCode1
`Just because you are right, doesn't mean I am wrong': Overcoming a bottleneck in development and evaluation of Open-Ended VQA tasks0
Towards General Purpose Vision SystemsCode1
UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training0
Are Bias Mitigation Techniques for Deep Learning Effective?Code1
Analysis on Image Set Visual Question Answering0
Domain-robust VQA with diverse datasets and methods but no target labels0
SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic EventsCode1
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder TransformersCode1
'Just because you are right, doesn't mean I am wrong': Overcoming a Bottleneck in the Development and Evaluation of Open-Ended Visual Question Answering (VQA) TasksCode0
Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models0
On the hidden treasure of dialog in video question answeringCode1
Visual Grounding Strategies for Text-Only Natural Language Processing0
Multi-Modal Answer Validation for Knowledge-Based VQACode1
How to Design Sample and Computationally Efficient VQA Models0
A Comprehensive Survey of Scene Graphs: Generation and Application0
Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQACode0
VMAF And Variants: Towards A Unified VQA0
Characterizing Misclassifications of Deep NLP Models0
RL-CSDia: Representation Learning of Computer Science Diagrams0
Select, Substitute, Search: A New Benchmark for Knowledge-Augmented Visual Question AnsweringCode0
Contextual Dropout: An Efficient Sample-Dependent Dropout ModuleCode0
Visual Question Answering: which investigated applications?Code0
Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues0
Learning Compositional Representation for Few-shot Visual Question Answering0
SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question AnsweringCode1
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout TransformerCode1
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual ConceptsCode1
Less is More: ClipBERT for Video-and-Language Learning via Sparse SamplingCode1
ViLT: Vision-and-Language Transformer Without Convolution or Region SupervisionCode1
Unifying Vision-and-Language Tasks via Text GenerationCode1
Answer Questions with Right Image Regions: A Visual Attention Regularization ApproachCode0
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games0
VisualMRC: Machine Reading Comprehension on Document ImagesCode1
Unanswerable Questions about Images and Texts0
Visual Question Answering based on Local-Scene-Aware Referring Expression Generation0
Understanding in Artificial Intelligence0
Latent Variable Models for Visual Question Answering0
Recent Advances in Video Question Answering: A Review of Datasets and Methods0
Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge0
Understanding the Role of Scene Graphs in Visual Question Answering0
Predicting Relative Depth between Objects from Semantic Features0
Self Supervision for Attention NetworksCode0
Transformers in Vision: A Survey0
Hierarchical Graph Attention Network for Few-Shot Visual-Semantic Learning0
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide ImagesCode1
Pano-AVQA: Grounded Audio-Visual Question Answering on 360deg VideosCode1
MDETR - Modulated Detection for End-to-End Multi-Modal UnderstandingCode2
TRAR: Routing the Attention Spans in Transformer for Visual Question AnsweringCode1
Unsupervised Curriculum Domain Adaptation for No-Reference Video Quality AssessmentCode1
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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