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
PaLM-E: An Embodied Multimodal Language ModelCode2
OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human PreferenceCode2
A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document UnderstandingCode2
RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language ModelsCode2
SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator TrajectoriesCode2
Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical PerspectivesCode2
OCRBench: On the Hidden Mystery of OCR in Large Multimodal ModelsCode2
Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question AnsweringCode2
Neighbourhood Representative Sampling for Efficient End-to-end Video Quality AssessmentCode2
Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language ModelCode2
MTVQA: Benchmarking Multilingual Text-Centric Visual Question AnsweringCode2
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and ResultsCode2
CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-ExpertsCode2
CoLLaVO: Crayon Large Language and Vision mOdelCode2
VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality AssessmentCode2
VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and DatasetCode2
NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving ScenarioCode2
PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical ImagingCode2
MiniGPT-Med: Large Language Model as a General Interface for Radiology DiagnosisCode2
MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference OptimizationCode2
Med-Flamingo: a Multimodal Medical Few-shot LearnerCode2
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image AnalysisCode2
VL-ICL Bench: The Devil in the Details of Multimodal In-Context LearningCode2
EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model EvaluationCode2
When Large Vision-Language Model Meets Large Remote Sensing Imagery: Coarse-to-Fine Text-Guided Token PruningCode2
MedPromptX: Grounded Multimodal Prompting for Chest X-ray DiagnosisCode2
LM4LV: A Frozen Large Language Model for Low-level Vision TasksCode2
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer LearningCode2
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question AnsweringCode2
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image UnderstandingCode2
MDETR - Modulated Detection for End-to-End Multi-Modal UnderstandingCode2
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction TuningCode2
HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language ModelsCode2
Grounding-IQA: Multimodal Language Grounding Model for Image Quality AssessmentCode2
BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual QuestionsCode2
GPT4RoI: Instruction Tuning Large Language Model on Region-of-InterestCode2
GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AICode2
KVQ: Kwai Video Quality Assessment for Short-form VideosCode2
Frontiers in Intelligent ColonoscopyCode2
GeReA: Question-Aware Prompt Captions for Knowledge-based Visual Question AnsweringCode2
Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in VideosCode2
GIT: A Generative Image-to-text Transformer for Vision and LanguageCode2
Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question AnsweringCode2
CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video ModelsCode2
Learning to Compose Dynamic Tree Structures for Visual ContextsCode2
Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language ModelsCode2
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language UnderstandingCode2
Evaluating Multimodal Representations on Visual Semantic Textual SimilarityCode1
Expectation-Maximization Contrastive Learning for Compact Video-and-Language RepresentationsCode1
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document 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
9InternVL-CAccuracy81.2Unverified
10LyricsAccuracy81.2Unverified
#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