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 701–725 of 2167 papers

TitleStatusHype
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark—0
C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset—0
Avoiding Barren Plateaus with Classical Deep Neural Networks—0
Analysis of Visual Question Answering Algorithms with attention model—0
Curriculum Script Distillation for Multilingual Visual Question Answering—0
Curriculum reinforcement learning for quantum architecture search under hardware errors—0
A Visual Question Answering Method for SAR Ship: Breaking the Requirement for Multimodal Dataset Construction and Model Fine-Tuning—0
InfographicVQA—0
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching—0
Curriculum Learning for Compositional Visual Reasoning—0
Curriculum Learning Effectively Improves Low Data VQA—0
A Vision Centric Remote Sensing Benchmark—0
CT-Agent: A Multimodal-LLM Agent for 3D CT Radiology Question Answering—0
AVIS: Autonomous Visual Information Seeking with Large Language Model Agent—0
CS-VQA: Visual Question Answering with Compressively Sensed Images—0
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization—0
Auto-Parsing Network for Image Captioning and Visual Question Answering—0
A Multimodal Memes Classification: A Survey and Open Research Issues—0
A dataset of clinically generated visual questions and answers about radiology images—0
2nd Place Solution to the GQA Challenge 2019—0
Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models—0
Cross-Modal Retrieval Augmentation for Multi-Modal Classification—0
Cross-modal Knowledge Reasoning for Knowledge-based Visual Question Answering—0
Cross-Modal Generative Augmentation for Visual Question Answering—0
American == White in Multimodal Language-and-Image AI—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