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
Learning to Discretely Compose Reasoning Module Networks for Video CaptioningCode1
Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video EnhancementCode1
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset BiasesCode1
Learning Cooperative Visual Dialog Agents with Deep Reinforcement LearningCode1
Does Vision-and-Language Pretraining Improve Lexical Grounding?Code1
Align before Fuse: Vision and Language Representation Learning with Momentum DistillationCode1
FiLM: Visual Reasoning with a General Conditioning LayerCode1
Combo of Thinking and Observing for Outside-Knowledge VQACode1
LIVE: Learnable In-Context Vector for Visual Question AnsweringCode1
Learning Situation Hyper-Graphs for Video Question AnsweringCode1
DocFormerv2: Local Features for Document UnderstandingCode1
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense CaptionerCode1
LaTr: Layout-Aware Transformer for Scene-Text VQACode1
FlowLearn: Evaluating Large Vision-Language Models on Flowchart UnderstandingCode1
FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food CultureCode1
Distilled Dual-Encoder Model for Vision-Language UnderstandingCode1
OK-VQA: A Visual Question Answering Benchmark Requiring External KnowledgeCode1
DocVQA: A Dataset for VQA on Document ImagesCode1
Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question AnsweringCode1
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data SynthesisCode1
Learning to Answer Questions in Dynamic Audio-Visual ScenariosCode1
Light-VQA+: A Video Quality Assessment Model for Exposure Correction with Vision-Language GuidanceCode1
Maintaining Reasoning Consistency in Compositional Visual Question AnsweringCode1
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connectionsCode1
ConceptBert: Concept-Aware Representation for Visual Question AnsweringCode1
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual ConceptsCode1
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQACode1
Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?Code1
Language-Informed Visual Concept LearningCode1
Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment and Motion-Aware ConsistencyCode1
Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question AnsweringCode1
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of PneumothoraxCode1
LaPA: Latent Prompt Assist Model For Medical Visual Question AnsweringCode1
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?Code1
Contrast and Classify: Training Robust VQA ModelsCode1
2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC VideosCode1
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K ResolutionCode1
A-OKVQA: A Benchmark for Visual Question Answering using World KnowledgeCode1
Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched PromptsCode1
Multimodal Inverse Cloze Task for Knowledge-based Visual Question AnsweringCode1
Large Language Models are Temporal and Causal Reasoners for Video Question AnsweringCode1
Can I Trust Your Answer? Visually Grounded Video Question AnsweringCode1
Counterfactual Samples Synthesizing and Training for Robust Visual Question AnsweringCode1
Counterfactual Samples Synthesizing for Robust Visual Question AnsweringCode1
Label-Descriptive Patterns and Their Application to Characterizing Classification ErrorsCode1
Counterfactual VQA: A Cause-Effect Look at Language BiasCode1
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question AnsweringCode1
Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-TrainingCode1
Detecting Hate Speech in Multi-modal MemesCode1
DeVLBert: Learning Deconfounded Visio-Linguistic RepresentationsCode1
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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