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 51100 of 2167 papers

TitleStatusHype
Vision-Language Pre-training: Basics, Recent Advances, and Future TrendsCode3
HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at ScaleCode3
CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal ReasoningCode3
Unifying Vision, Text, and Layout for Universal Document ProcessingCode3
TinyGPT-V: Efficient Multimodal Large Language Model via Small BackbonesCode3
Towards VQA Models That Can ReadCode3
Emu: Generative Pretraining in MultimodalityCode3
An Empirical Study on Prompt Compression for Large Language ModelsCode3
Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth FusionCode3
Evaluating Text-to-Visual Generation with Image-to-Text GenerationCode3
Bilinear Attention NetworksCode3
OCR-free Document Understanding TransformerCode3
MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language ModelsCode3
PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal RetrieversCode3
DriveLM: Driving with Graph Visual Question AnsweringCode3
MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for MedicineCode3
Pythia v0.1: the Winning Entry to the VQA Challenge 2018Code3
Vision-Language Models for Medical Report Generation and Visual Question Answering: A ReviewCode3
PaLM-E: An Embodied Multimodal Language ModelCode2
PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image UnderstandingCode2
Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question AnsweringCode2
Oscar: Object-Semantics Aligned Pre-training for Vision-Language TasksCode2
PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical ImagingCode2
OCRBench: On the Hidden Mystery of OCR in Large Multimodal ModelsCode2
OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human PreferenceCode2
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language UnderstandingCode2
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
CoLLaVO: Crayon Large Language and Vision mOdelCode2
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and ResultsCode2
MTVQA: Benchmarking Multilingual Text-Centric Visual Question AnsweringCode2
All in One: Exploring Unified Video-Language Pre-trainingCode2
Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language ModelsCode2
NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving ScenarioCode2
PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical DocumentsCode2
MedPromptX: Grounded Multimodal Prompting for Chest X-ray DiagnosisCode2
Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction TuningCode2
MDETR - Modulated Detection for End-to-End Multi-Modal UnderstandingCode2
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer LearningCode2
LM4LV: A Frozen Large Language Model for Low-level Vision TasksCode2
A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document UnderstandingCode2
MiniGPT-Med: Large Language Model as a General Interface for Radiology DiagnosisCode2
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image AnalysisCode2
Med-Flamingo: a Multimodal Medical Few-shot LearnerCode2
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question AnsweringCode2
CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video ModelsCode2
Learning to Compose Dynamic Tree Structures for Visual ContextsCode2
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction TuningCode2
KVQ: Kwai Video Quality Assessment for Short-form VideosCode2
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image UnderstandingCode2
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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