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
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
VL-ICL Bench: The Devil in the Details of Multimodal In-Context LearningCode2
HYDRA: A Hyper Agent for Dynamic Compositional Visual ReasoningCode1
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
PhD: A ChatGPT-Prompted Visual hallucination Evaluation DatasetCode1
Knowledge Condensation and Reasoning for Knowledge-based VQA—0
Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning—0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
UniCode: Learning a Unified Codebook for Multimodal Large Language Models—0
Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question AnsweringCode0
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal ModelsCode1
Multi-modal Auto-regressive Modeling via Visual WordsCode1
OmniCount: Multi-label Object Counting with Semantic-Geometric Priors—0
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM—0
TextMonkey: An OCR-Free Large Multimodal Model for Understanding DocumentCode5
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments—0
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation—0
Vision-Language Models for Medical Report Generation and Visual Question Answering: A ReviewCode3
ArcSin: Adaptive ranged cosine Similarity injected noise for Language-Driven Visual Tasks—0
LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic SurgeryCode0
Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQACode1
CommVQA: Situating Visual Question Answering in Communicative ContextsCode0
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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