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 351400 of 2167 papers

TitleStatusHype
Hierarchical multimodal transformers for Multi-Page DocVQACode1
Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual ReasoningCode1
Seeing What You Miss: Vision-Language Pre-training with Semantic Completion LearningCode1
Self-supervised vision-language pretraining for Medical visual question answeringCode1
Expectation-Maximization Contrastive Learning for Compact Video-and-Language RepresentationsCode1
I Can't Believe There's No Images! Learning Visual Tasks Using only Language SupervisionCode1
PromptCap: Prompt-Guided Task-Aware Image CaptioningCode1
MapQA: A Dataset for Question Answering on Choropleth MapsCode1
Visual Named Entity Linking: A New Dataset and A BaselineCode1
VLC-BERT: Visual Question Answering with Contextualized Commonsense KnowledgeCode1
Meta-Learning via Classifier(-free) Diffusion GuidanceCode1
MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot PromptingCode1
SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric ModelsCode1
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document UnderstandingCode1
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training ModelCode1
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive LearningCode1
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQACode1
Linearly Mapping from Image to Text SpaceCode1
TVLT: Textless Vision-Language TransformerCode1
Towards Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong BaselineCode1
Panoramic Vision Transformer for Saliency Detection in 360° VideosCode1
MaXM: Towards Multilingual Visual Question AnsweringCode1
An Empirical Study of End-to-End Video-Language Transformers with Masked Visual ModelingCode1
2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC VideosCode1
Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical AlignmentCode1
Symbolic Replay: Scene Graph as Prompt for Continual Learning on VQA TaskCode1
CLEVR-Math: A Dataset for Compositional Language, Visual and Mathematical ReasoningCode1
ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal UnderstandingCode1
TAG: Boosting Text-VQA via Text-aware Visual Question-answer GenerationCode1
Generative Bias for Robust Visual Question AnsweringCode1
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question AnsweringCode1
LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text InjectionCode1
Rethinking Data Augmentation for Robust Visual Question AnsweringCode1
Clover: Towards A Unified Video-Language Alignment and Fusion ModelCode1
Video Graph Transformer for Video Question AnsweringCode1
ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named EntitiesCode1
Weakly Supervised Grounding for VQA in Vision-Language TransformersCode1
A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQACode1
Consistency-preserving Visual Question Answering in Medical ImagingCode1
Surgical-VQA: Visual Question Answering in Surgical Scenes using TransformerCode1
Zero-Shot Video Question Answering via Frozen Bidirectional Language ModelsCode1
MixGen: A New Multi-Modal Data AugmentationCode1
Coarse-to-Fine Vision-Language Pre-training with Fusion in the BackboneCode1
A-OKVQA: A Benchmark for Visual Question Answering using World KnowledgeCode1
REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question AnsweringCode1
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connectionsCode1
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language ModelsCode1
Learning to Answer Visual Questions from Web VideosCode1
Declaration-based Prompt Tuning for Visual Question AnsweringCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
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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