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
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations—0
Proximity QA: Unleashing the Power of Multi-Modal Large Language Models for Spatial Proximity AnalysisCode0
Common Sense Reasoning for Deepfake DetectionCode3
LCV2: An Efficient Pretraining-Free Framework for Grounded Visual Question Answering—0
Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA—0
Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning—0
Free Form Medical Visual Question Answering in Radiology—0
SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities—0
Q&A Prompts: Discovering Rich Visual Clues through Mining Question-Answer Prompts for VQA requiring Diverse World KnowledgeCode1
Veagle: Advancements in Multimodal Representation LearningCode1
Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation—0
Question-Answer Cross Language Image Matching for Weakly Supervised Semantic SegmentationCode1
COCO is "ALL'' You Need for Visual Instruction Fine-tuning—0
Video Quality Assessment Based on Swin TransformerV2 and Coarse to Fine Strategy—0
Uncovering the Full Potential of Visual Grounding Methods in VQACode0
BOK-VQA: Bilingual outside Knowledge-Based Visual Question Answering via Graph Representation Pretraining—0
Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language ModelCode0
Cross-modal Retrieval for Knowledge-based Visual Question AnsweringCode1
Hallucination Benchmark in Medical Visual Question AnsweringCode0
MISS: A Generative Pretraining and Finetuning Approach for Med-VQACode1
GRAM: Global Reasoning for Multi-Page VQA—0
3DMIT: 3D Multi-modal Instruction Tuning for Scene UnderstandingCode1
Subjective and Objective Analysis of Indian Social Media Video QualityCode0
PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical ImagingCode2
ArtQuest: Countering Hidden Language Biases in ArtVQACode0
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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