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

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Showing 851–900 of 2167 papers

TitleStatusHype
Visual question answering based evaluation metrics for text-to-image generation—0
Is Cognition consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding—0
SparrowVQE: Visual Question Explanation for Course Content UnderstandingCode0
Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models—0
Select2Plan: Training-Free ICL-Based Planning through VQA and Memory Retrieval—0
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA—0
Multimodal Commonsense Knowledge Distillation for Visual Question Answering—0
MME-Finance: A Multimodal Finance Benchmark for Expert-level Understanding and Reasoning—0
One VLM to Keep it Learning: Generation and Balancing for Data-free Continual Visual Question Answering—0
A Visual Question Answering Method for SAR Ship: Breaking the Requirement for Multimodal Dataset Construction and Model Fine-Tuning—0
Goal-Oriented Semantic Communication for Wireless Visual Question Answering—0
Right this way: Can VLMs Guide Us to See More to Answer Questions?Code0
Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP—0
SimpsonsVQA: Enhancing Inquiry-Based Learning with a Tailored Dataset—0
Are VLMs Really BlindCode0
Improving Generalization in Visual Reasoning via Self-Ensemble—0
AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?Code0
Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!—0
Few-Shot Multimodal Explanation for Visual Question AnsweringCode0
Efficient Bilinear Attention-based Fusion for Medical Visual Question Answering—0
R-LLaVA: Improving Med-VQA Understanding through Visual Region of Interest—0
GPT-4o System Card—0
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering—0
Visual Question Answering in Ophthalmology: A Progressive and Practical Perspective—0
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models—0
LLaVA-Ultra: Large Chinese Language and Vision Assistant for Ultrasound—0
ChitroJera: A Regionally Relevant Visual Question Answering Dataset for Bangla—0
SemiHVision: Enhancing Medical Multimodal Models with a Semi-Human Annotated Dataset and Fine-Tuned Instruction GenerationCode0
NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples—0
ViConsFormer: Constituting Meaningful Phrases of Scene Texts using Transformer-based Method in Vietnamese Text-based Visual Question AnsweringCode0
Latent Image and Video Resolution Prediction using Convolutional Neural Networks—0
Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts?Code0
RescueADI: Adaptive Disaster Interpretation in Remote Sensing Images with Autonomous Agents—0
ActionCOMET: A Zero-shot Approach to Learn Image-specific Commonsense Concepts about ActionsCode0
Difficult Task Yes but Simple Task No: Unveiling the Laziness in Multimodal LLMsCode0
SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding—0
Eliminating the Language Bias for Visual Question Answering with fine-grained Causal Intervention—0
Declarative Knowledge Distillation from Large Language Models for Visual Question Answering DatasetsCode0
Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities—0
ViT3D Alignment of LLaMA3: 3D Medical Image Report Generation—0
Secure Video Quality Assessment Resisting Adversarial Attacks—0
Beyond Captioning: Task-Specific Prompting for Improved VLM Performance in Mathematical Reasoning—0
ERVQA: A Dataset to Benchmark the Readiness of Large Vision Language Models in Hospital EnvironmentsCode0
TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable QuestionsCode0
Video Instruction Tuning With Synthetic Data—0
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—0
Backdooring Vision-Language Models with Out-Of-Distribution Data—0
Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities—0
BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured DataCode0
FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks—0
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All datasetsGQA Test2019VQA v2 test-devVQA v2 test-stdOK-VQAMSVD-QADocVQA testMSRVTT-QAInfographicVQAGQA test-devVizWiz 2020 VQAA-OKVQACLEVR

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1humanAccuracy89.3—Unverified
2DREAM+Unicoder-VL (MSRA)Accuracy76.04—Unverified
3TRRNet (Ensemble)Accuracy74.03—Unverified
4MIL-nbgaoAccuracy73.81—Unverified
5Kakao BrainAccuracy73.33—Unverified
6Coarse-to-Fine Reasoning, Single ModelAccuracy72.14—Unverified
7270Accuracy70.23—Unverified
8NSM ensemble (updated)Accuracy67.55—Unverified
9VinVL-DPTAccuracy64.92—Unverified
10VinVL+LAccuracy64.85—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLIAccuracy84.3—Unverified
2BEiT-3Accuracy84.19—Unverified
3VLMoAccuracy82.78—Unverified
4ONE-PEACEAccuracy82.6—Unverified
5mPLUG (Huge)Accuracy82.43—Unverified
6CuMo-7BAccuracy82.2—Unverified
7X2-VLM (large)Accuracy81.9—Unverified
8MMUAccuracy81.26—Unverified
9LyricsAccuracy81.2—Unverified
10InternVL-CAccuracy81.2—Unverified
#ModelMetricClaimedVerifiedStatus
1BEiT-3overall84.03—Unverified
2mPLUG-Hugeoverall83.62—Unverified
3ONE-PEACEoverall82.52—Unverified
4X2-VLM (large)overall81.8—Unverified
5VLMooverall81.3—Unverified
6SimVLMoverall80.34—Unverified
7X2-VLM (base)overall80.2—Unverified
8VASToverall80.19—Unverified
9VALORoverall78.62—Unverified
10Prompt Tuningoverall78.53—Unverified