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 26–50 of 2167 papers

TitleStatusHype
CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video ModelsCode2
Outside Knowledge Conversational Video (OKCV) Dataset -- Dialoguing over VideosCode0
Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal EndoscopyCode0
PhyBlock: A Progressive Benchmark for Physical Understanding and Planning via 3D Block Assembly—0
From Pixels to Graphs: using Scene and Knowledge Graphs for HD-EPIC VQA Challenge—0
Looking Beyond Visible Cues: Implicit Video Question Answering via Dual-Clue ReasoningCode0
HAIBU-ReMUD: Reasoning Multimodal Ultrasound Dataset and Model Bridging to General Specific DomainsCode0
Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning—0
Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems—0
ReXVQA: A Large-scale Visual Question Answering Benchmark for Generalist Chest X-ray Understanding—0
CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG—0
Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering—0
MMedAgent-RL: Optimizing Multi-Agent Collaboration for Multimodal Medical Reasoning—0
Vision LLMs Are Bad at Hierarchical Visual Understanding, and LLMs Are the Bottleneck—0
Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting—0
VideoCAD: A Large-Scale Video Dataset for Learning UI Interactions and 3D Reasoning from CAD SoftwareCode1
A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis—0
MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence—0
Spoken question answering for visual queries—0
Synthetic Document Question Answering in HungarianCode0
Multi-Sourced Compositional Generalization in Visual Question AnsweringCode0
Interpreting Chest X-rays Like a Radiologist: A Benchmark with Clinical ReasoningCode1
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
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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
9InternVL-CAccuracy81.2—Unverified
10LyricsAccuracy81.2—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