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 1026–1050 of 2167 papers

TitleStatusHype
MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationale—0
Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models—0
ViTextVQA: A Large-Scale Visual Question Answering Dataset for Evaluating Vietnamese Text Comprehension in ImagesCode0
Find The Gap: Knowledge Base Reasoning For Visual Question Answering—0
Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMsCode0
BRAVE: Broadening the visual encoding of vision-language models—0
OmniFusion Technical ReportCode0
HAMMR: HierArchical MultiModal React agents for generic VQA—0
Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language ModelsCode0
Study of the effect of Sharpness on Blind Video Quality Assessment—0
BuDDIE: A Business Document Dataset for Multi-task Information Extraction—0
TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices—0
Detect2Interact: Localizing Object Key Field in Visual Question Answering (VQA) with LLMs—0
Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-trainingCode0
Visual Hallucination: Definition, Quantification, and Prescriptive Remediations—0
Intrinsic Subgraph Generation for Interpretable Graph based Visual Question AnsweringCode0
A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions—0
Synthesize Step-by-Step: Tools, Templates and LLMs as Data Generators for Reasoning-Based Chart VQA—0
Surgical-LVLM: Learning to Adapt Large Vision-Language Model for Grounded Visual Question Answering in Robotic Surgery—0
Multi-Modal Hallucination Control by Visual Information Grounding—0
AGFSync: Leveraging AI-Generated Feedback for Preference Optimization in Text-to-Image Generation—0
WoLF: Wide-scope Large Language Model Framework for CXR Understanding—0
SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors—0
FlexCap: Describe Anything in Images in Controllable Detail—0
Few-Shot VQA with Frozen LLMs: A Tale of Two Approaches—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