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 1151–1175 of 2167 papers

TitleStatusHype
Accounting for Focus Ambiguity in Visual Questions—0
Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models—0
Achieving Human Parity on Visual Question Answering—0
A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment—0
A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis—0
A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis—0
A Comprehensive Survey of Knowledge-Based Vision Question Answering Systems: The Lifecycle of Knowledge in Visual Reasoning Task—0
A Comprehensive Survey on Visual Question Answering Datasets and Algorithms—0
A Confidence-Based Interface for Neuro-Symbolic Visual Question Answering—0
A Corpus for Visual Question Answering Annotated with Frame Semantic Information—0
A Corpus of Natural Language for Visual Reasoning—0
Action Verb Corpus—0
Actively Seeking and Learning from Live Data—0
Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment—0
A Dataset for Multimodal Question Answering in the Cultural Heritage Domain—0
A dataset of clinically generated visual questions and answers about radiology images—0
``A Distorted Skull Lies in the Bottom Center...'' Identifying Paintings from Text Descriptions—0
Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation—0
Advancing Multimodal Medical Capabilities of Gemini—0
Advancing Surgical VQA with Scene Graph Knowledge—0
Advancing Video Quality Assessment for AIGC—0
AdvDreamer Unveils: Are Vision-Language Models Truly Ready for Real-World 3D Variations?—0
Adventurer's Treasure Hunt: A Transparent System for Visually Grounded Compositional Visual Question Answering based on Scene Graphs—0
Adversarial Attacks Beyond the Image Space—0
Adversarial Multimodal Network for Movie Question Answering—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