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 201250 of 2167 papers

TitleStatusHype
V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?Code1
FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis AssistantCode1
Visual Agents as Fast and Slow ThinkersCode1
Surgical-VQLA++: Adversarial Contrastive Learning for Calibrated Robust Visual Question-Localized Answering in Robotic SurgeryCode1
Visual Haystacks: A Vision-Centric Needle-In-A-Haystack BenchmarkCode1
ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality AssessmentCode1
FlowLearn: Evaluating Large Vision-Language Models on Flowchart UnderstandingCode1
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data SynthesisCode1
STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-AnsweringCode1
LIVE: Learnable In-Context Vector for Visual Question AnsweringCode1
MMNeuron: Discovering Neuron-Level Domain-Specific Interpretation in Multimodal Large Language ModelCode1
FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food CultureCode1
Vision-Language Models Meet Meteorology: Developing Models for Extreme Weather Events Detection with HeatmapsCode1
Worse than Random? An Embarrassingly Simple Probing Evaluation of Large Multimodal Models in Medical VQACode1
Instruction-Guided Visual MaskingCode1
Reverse Image Retrieval Cues Parametric Memory in Multimodal LLMsCode1
PitVQA: Image-grounded Text Embedding LLM for Visual Question Answering in Pituitary SurgeryCode1
Light-VQA+: A Video Quality Assessment Model for Exposure Correction with Vision-Language GuidanceCode1
ViOCRVQA: Novel Benchmark Dataset and Vision Reader for Visual Question Answering by Understanding Vietnamese Text in ImagesCode1
LaPA: Latent Prompt Assist Model For Medical Visual Question AnsweringCode1
TextCoT: Zoom In for Enhanced Multimodal Text-Rich Image UnderstandingCode1
Enhancing Visual Question Answering through Question-Driven Image Captions as PromptsCode1
JDocQA: Japanese Document Question Answering Dataset for Generative Language ModelsCode1
Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal PerspectiveCode1
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language ModelsCode1
Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question AnsweringCode1
HYDRA: A Hyper Agent for Dynamic Compositional Visual ReasoningCode1
PhD: A ChatGPT-Prompted Visual hallucination Evaluation DatasetCode1
Multi-modal Auto-regressive Modeling via Visual WordsCode1
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal ModelsCode1
Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQACode1
Uncertainty-Aware Evaluation for Vision-Language ModelsCode1
Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge AlignmentCode1
Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language ModelsCode1
Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchyCode1
Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & HallucinationsCode1
Text-Guided Image ClusteringCode1
Q&A Prompts: Discovering Rich Visual Clues through Mining Question-Answer Prompts for VQA requiring Diverse World KnowledgeCode1
Question-Answer Cross Language Image Matching for Weakly Supervised Semantic SegmentationCode1
Veagle: Advancements in Multimodal Representation LearningCode1
Cross-modal Retrieval for Knowledge-based Visual Question AnsweringCode1
MISS: A Generative Pretraining and Finetuning Approach for Med-VQACode1
3DMIT: 3D Multi-modal Instruction Tuning for Scene UnderstandingCode1
Mining Fine-Grained Image-Text Alignment for Zero-Shot Captioning via Text-Only TrainingCode1
Towards More Faithful Natural Language Explanation Using Multi-Level Contrastive Learning in VQACode1
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic TasksCode1
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question AnsweringCode1
HAAR: Text-Conditioned Generative Model of 3D Strand-based Human HairstylesCode1
ViLA: Efficient Video-Language Alignment for Video Question AnsweringCode1
Genixer: Empowering Multimodal Large Language Models as a Powerful Data GeneratorCode1
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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