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 101150 of 2167 papers

TitleStatusHype
MTVQA: Benchmarking Multilingual Text-Centric Visual Question AnsweringCode2
CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-ExpertsCode2
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and ResultsCode2
Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language ModelsCode2
Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language ModelsCode2
MedPromptX: Grounded Multimodal Prompting for Chest X-ray DiagnosisCode2
vid-TLDR: Training Free Token merging for Light-weight Video TransformerCode2
VL-ICL Bench: The Devil in the Details of Multimodal In-Context LearningCode2
CoLLaVO: Crayon Large Language and Vision mOdelCode2
KVQ: Kwai Video Quality Assessment for Short-form VideosCode2
ScreenAI: A Vision-Language Model for UI and Infographics UnderstandingCode2
GeReA: Question-Aware Prompt Captions for Knowledge-based Visual Question AnsweringCode2
PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical ImagingCode2
Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined LevelsCode2
HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language ModelsCode2
Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question AnsweringCode2
BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual QuestionsCode2
TeCH: Text-guided Reconstruction of Lifelike Clothed HumansCode2
Med-Flamingo: a Multimodal Medical Few-shot LearnerCode2
GPT4RoI: Instruction Tuning Large Language Model on Region-of-InterestCode2
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image UnderstandingCode2
Shikra: Unleashing Multimodal LLM's Referential Dialogue MagicCode2
Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction TuningCode2
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and DatasetCode2
NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving ScenarioCode2
OCRBench: On the Hidden Mystery of OCR in Large Multimodal ModelsCode2
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction TuningCode2
VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and DatasetCode2
PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical DocumentsCode2
PaLM-E: An Embodied Multimodal Language ModelCode2
Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question AnsweringCode2
X^2-VLM: All-In-One Pre-trained Model For Vision-Language TasksCode2
Visual Programming: Compositional visual reasoning without trainingCode2
Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical PerspectivesCode2
PoseScript: Linking 3D Human Poses and Natural LanguageCode2
Neighbourhood Representative Sampling for Efficient End-to-end Video Quality AssessmentCode2
Retrieval Augmented Visual Question Answering with Outside KnowledgeCode2
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language UnderstandingCode2
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question AnsweringCode2
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer LearningCode2
GIT: A Generative Image-to-text Transformer for Vision and LanguageCode2
All in One: Exploring Unified Video-Language Pre-trainingCode2
Vision-Language Pre-Training with Triple Contrastive LearningCode2
MDETR - Modulated Detection for End-to-End Multi-Modal UnderstandingCode2
Oscar: Object-Semantics Aligned Pre-training for Vision-Language TasksCode2
Unified Vision-Language Pre-Training for Image Captioning and VQACode2
Learning to Compose Dynamic Tree Structures for Visual ContextsCode2
Describe Anything Model for Visual Question Answering on Text-rich ImagesCode1
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and GrounderCode1
VideoCAD: A Large-Scale Video Dataset for Learning UI Interactions and 3D Reasoning from CAD SoftwareCode1
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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