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 651–675 of 2167 papers

TitleStatusHype
SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision TasksCode0
Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning—0
Robo2VLM: Visual Question Answering from Large-Scale In-the-Wild Robot Manipulation Datasets—0
Visual Question Answering on Multiple Remote Sensing Image Modalities—0
CP-LLM: Context and Pixel Aware Large Language Model for Video Quality Assessment—0
TinyDrive: Multiscale Visual Question Answering with Selective Token Routing for Autonomous Driving—0
Debating for Better Reasoning: An Unsupervised Multimodal Approach—0
PlanGPT-VL: Enhancing Urban Planning with Domain-Specific Vision-Language Models—0
Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models—0
TinyRS-R1: Compact Multimodal Language Model for Remote Sensing—0
MedSG-Bench: A Benchmark for Medical Image Sequences Grounding—0
RVTBench: A Benchmark for Visual Reasoning TasksCode0
HumaniBench: A Human-Centric Framework for Large Multimodal Models EvaluationCode0
Semantically-Aware Game Image Quality Assessment—0
TCC-Bench: Benchmarking the Traditional Chinese Culture Understanding Capabilities of MLLMsCode0
Enhancing Multi-Image Question Answering via Submodular Subset Selection—0
Variational Visual Question Answering—0
OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval—0
Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving—0
R^3-VQA: "Read the Room" by Video Social Reasoning—0
DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor—0
Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-SupervisionCode0
AOR: Anatomical Ontology-Guided Reasoning for Medical Large Multimodal Model in Chest X-Ray Interpretation—0
Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networks—0
SpatialLLM: A Compound 3D-Informed Design towards Spatially-Intelligent Large Multimodal Models—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