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 626–650 of 2167 papers

TitleStatusHype
Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting—0
Multi-Sourced Compositional Generalization in Visual Question AnsweringCode0
A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis—0
Spoken question answering for visual queries—0
Synthetic Document Question Answering in HungarianCode0
MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence—0
NegVQA: Can Vision Language Models Understand Negation?—0
VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language ModelsCode0
FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question AnsweringCode0
Silence is Not Consensus: Disrupting Agreement Bias in Multi-Agent LLMs via Catfish Agent for Clinical Decision Making—0
Diagnosing and Mitigating Modality Interference in Multimodal Large Language ModelsCode0
Benchmarking Large Multimodal Models for Ophthalmic Visual Question Answering with OphthalWeChat—0
MMIG-Bench: Towards Comprehensive and Explainable Evaluation of Multi-Modal Image Generation Models—0
TDVE-Assessor: Benchmarking and Evaluating the Quality of Text-Driven Video Editing with LMMs—0
Medical Large Vision Language Models with Multi-Image Visual AbilityCode0
GC-KBVQA: A New Four-Stage Framework for Enhancing Knowledge Based Visual Question Answering Performance—0
NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and ResultsCode0
Improving Medical Reasoning with Curriculum-Aware Reinforcement Learning—0
Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning—0
CT-Agent: A Multimodal-LLM Agent for 3D CT Radiology Question Answering—0
A Causal Approach to Mitigate Modality Preference Bias in Medical Visual Question Answering—0
Grounding Chest X-Ray Visual Question Answering with Generated Radiology Reports—0
Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation—0
MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning—0
Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge—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