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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1476–1500 of 2167 papers

TitleStatusHype
On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering—0
Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training—0
COIN: Counterfactual Image Generation for VQA Interpretation—0
Interactive Attention AI to translate low light photos to captions for night scene understanding in women safety—0
Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering—0
Query and Attention Augmentation for Knowledge-Based Explainable ReasoningCode0
Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture—0
V-Doc: Visual Questions Answers With Documents—0
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?—0
Multi-Image Visual Question AnsweringCode0
General Greedy De-bias LearningCode0
Task-Oriented Multi-User Semantic Communications—0
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey—0
Understanding Attention for Vision-and-Language Tasks—0
3D Question Answering—0
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection—0
Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation—0
MoCA: Incorporating Multi-stage Domain Pretraining and Cross-guided Multimodal Attention for Textbook Question Answering—0
Curriculum Learning Effectively Improves Low Data VQA—0
Robust Visual Reasoning via Language Guided Neural Module Networks—0
eaVQA: An Experimental Analysis on Visual Question Answering Models—0
Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning—0
LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering—0
Scene Graph Generation with Geometric Context—0
A Confidence-Based Interface for Neuro-Symbolic Visual Question Answering—0
Show:102550
← PrevPage 60 of 87Next →
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