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 601–625 of 2167 papers

TitleStatusHype
3D Concept Learning and Reasoning from Multi-View Images—0
Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think!—0
AdvDreamer Unveils: Are Vision-Language Models Truly Ready for Real-World 3D Variations?—0
High Frame Rate Video Quality Assessment using VMAF and Entropic Differences—0
Document Visual Question Answering Challenge 2020—0
An Empirical Study on the Language Modal in Visual Question Answering—0
Document Collection Visual Question Answering—0
A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis—0
Highly Efficient No-reference 4K Video Quality Assessment with Full-Pixel Covering Sampling and Training Strategy—0
How good are deep models in understanding the generated images?—0
Document AI: Benchmarks, Models and Applications—0
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games—0
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations—0
Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback—0
A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis—0
Hierarchical Memory for Long Video QA—0
Diversity and Consistency: Exploring Visual Question-Answer Pair Generation—0
Advancing Video Quality Assessment for AIGC—0
Distraction-free Embeddings for Robust VQA—0
Hierarchical Graph Attention Network for Few-Shot Visual-Semantic Learning—0
Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion—0
Disentangling Knowledge-based and Visual Reasoning by Question Decomposition in KB-VQA—0
Beyond VQA: Generating Multi-word Answer and Rationale to Visual Questions—0
An Empirical Study on Leveraging Scene Graphs for Visual Question Answering—0
Directional Gradient Projection for Robust Fine-Tuning of Foundation 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