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 401450 of 2167 papers

TitleStatusHype
Faithful Multimodal Explanation for Visual Question AnsweringCode1
CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractionsCode1
A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQACode1
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMsCode1
A Dataset and Baselines for Visual Question Answering on ArtCode1
CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language TransformersCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
AMD-Hummingbird: Towards an Efficient Text-to-Video ModelCode1
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question AnsweringCode1
Dynamic Language Binding in Relational Visual ReasoningCode1
OK-VQA: A Visual Question Answering Benchmark Requiring External KnowledgeCode1
LXMERT: Learning Cross-Modality Encoder Representations from TransformersCode1
Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model EvaluationCode1
Panoramic Vision Transformer for Saliency Detection in 360° VideosCode1
MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot PromptingCode1
In Defense of Grid Features for Visual Question AnsweringCode1
Dual-Key Multimodal Backdoors for Visual Question AnsweringCode1
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language ReasoningCode1
Analysis of Video Quality Datasets via Design of Minimalistic Video Quality ModelsCode1
Localized Questions in Medical Visual Question AnsweringCode1
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language ModelsCode1
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language ModelsCode1
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?Code1
A-OKVQA: A Benchmark for Visual Question Answering using World KnowledgeCode1
PMC-VQA: Visual Instruction Tuning for Medical Visual Question AnsweringCode1
LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question AnsweringCode1
Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of ExpertsCode1
Improving Selective Visual Question Answering by Learning from Your PeersCode1
Can I Trust Your Answer? Visually Grounded Video Question AnsweringCode1
Prismer: A Vision-Language Model with Multi-Task ExpertsCode1
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question AnsweringCode1
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real ImagesCode1
Does Vision-and-Language Pretraining Improve Lexical Grounding?Code1
DataEnvGym: Data Generation Agents in Teacher Environments with Student FeedbackCode1
DocVQA: A Dataset for VQA on Document ImagesCode1
ProTo: Program-Guided Transformer for Program-Guided TasksCode1
Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question AnsweringCode1
Instruction-Guided Visual MaskingCode1
LiveXiv -- A Multi-Modal Live Benchmark Based on Arxiv Papers ContentCode1
Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video EnhancementCode1
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic TasksCode1
Debiased Visual Question Answering from Feature and Sample PerspectivesCode1
Debiasing Multimodal Models via Causal Information MinimizationCode1
Declaration-based Prompt Tuning for Visual Question AnsweringCode1
DocFormerv2: Local Features for Document UnderstandingCode1
Light-VQA+: A Video Quality Assessment Model for Exposure Correction with Vision-Language GuidanceCode1
Visual Grounding Methods for VQA are Working for the Wrong Reasons!Code1
ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality AssessmentCode1
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and GrounderCode1
Distilled Dual-Encoder Model for Vision-Language UnderstandingCode1
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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