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 16011650 of 2167 papers

TitleStatusHype
Chop Chop BERT: Visual Question Answering by Chopping VisualBERT's Heads0
Optimal training of variational quantum algorithms without barren plateausCode0
Document Collection Visual Question Answering0
InfographicVQA0
Playing Lottery Tickets with Vision and Language0
VGNMN: Video-grounded Neural Module Network to Video-Grounded Language Tasks0
Cross-Modal Retrieval Augmentation for Multi-Modal Classification0
Jointly Learning Truth-Conditional Denotations and Groundings using Parallel Attention0
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation0
Neuro-Symbolic VQA: A review from the perspective of AGI desiderata0
CLEVR_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over ImagesCode0
How Transferable are Reasoning Patterns in VQA?0
Multimodal Continuous Visual Attention Mechanisms0
Compressing Visual-linguistic Model via Knowledge Distillation0
`Just because you are right, doesn't mean I am wrong': Overcoming a bottleneck in development and evaluation of Open-Ended VQA tasks0
UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training0
Analysis on Image Set Visual Question Answering0
Domain-robust VQA with diverse datasets and methods but no target labels0
'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
Visual Grounding Strategies for Text-Only Natural Language Processing0
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
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
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
Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge0
Recent Advances in Video Question Answering: A Review of Datasets and Methods0
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
Differentiable End-to-End Program Executor for Sample and Computationally Efficient VQA0
Hierarchical Graph Attention Network for Few-Shot Visual-Semantic Learning0
Erasure for Advancing: Dynamic Self-Supervised Learning for Commonsense Reasoning0
Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering0
Unshuffling Data for Improved Generalization in Visual Question Answering0
Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings0
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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