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 16511700 of 2167 papers

TitleStatusHype
Applying recent advances in Visual Question Answering to Record LinkageCode0
Image Captioning with Compositional Neural Module Networks0
IQ-VQA: Intelligent Visual Question AnsweringCode0
Eliminating Catastrophic Interference with Biased Competition0
Visual Question Answering as a Multi-Task Problem0
Scene Graph Reasoning for Visual Question Answering0
The Impact of Explanations on AI Competency Prediction in VQA0
DocVQA: A Dataset for VQA on Document ImagesCode1
Towards Visual Dialog for Radiology0
Visual Question Generation from Radiology ImagesCode1
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering0
Multimodal Neural Graph Memory Networks for Visual Question Answering0
ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph0
Ontology-guided Semantic Composition for Zero-Shot LearningCode1
Improving VQA and its Explanations \\ by Comparing Competing Explanations0
Graph Optimal Transport for Cross-Domain AlignmentCode1
Self-Segregating and Coordinated-Segregating Transformer for Focused Deep Multi-Modular Network for Visual Question Answering0
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"0
Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering0
ORD: Object Relationship Discovery for Visual Dialogue Generation0
Sparse and Continuous Attention MechanismsCode1
Large-Scale Adversarial Training for Vision-and-Language Representation LearningCode1
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic ReasoningCode1
Exploring Weaknesses of VQA Models through Attribution Driven Insights0
Estimating semantic structure for the VQA answer space0
Roses Are Red, Violets Are Blue... but Should Vqa Expect Them To?Code1
Counterfactual VQA: A Cause-Effect Look at Language BiasCode1
TA-Student VQA: Multi-Agents Training by Self-Questioning0
Counterfactual Vision and Language Learning0
Attention-Based Context Aware Reasoning for Situation RecognitionCode1
Structured Multimodal Attentions for TextVQACode1
Multimodal grid features and cell pointers for Scene Text Visual Question Answering0
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated ContentCode1
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law0
Visual Relationship Detection using Scene Graphs: A Survey0
Cross-Modality Relevance for Reasoning on Language and VisionCode1
COBRA: Contrastive Bi-Modal Representation AlgorithmCode1
Visual Question Answering with Prior Class Semantics0
A Corpus for Visual Question Answering Annotated with Frame Semantic Information0
Image Position Prediction in Multimodal Documents0
Visuo-Linguistic Question Answering (VLQA) ChallengeCode0
Dynamic Language Binding in Relational Visual ReasoningCode1
Pragmatic Issue-Sensitive Image CaptioningCode0
A Novel Attention-based Aggregation Function to Combine Vision and Language0
Deep Multimodal Neural Architecture SearchCode1
MoVie: Revisiting Modulated Convolutions for Visual Counting and BeyondCode0
Visual Question Answering Using Semantic Information from Image Descriptions0
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision0
Are we pretraining it right? Digging deeper into visio-linguistic pretraining0
Knowledge-Based Visual Question Answering in Videos0
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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