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 1301–1350 of 2167 papers

TitleStatusHype
Visual Question Answering From Another Perspective: CLEVR Mental Rotation TestsCode0
Compound Tokens: Channel Fusion for Vision-Language Representation Learning—0
Semi-supervised Learning of Perceptual Video Quality by Generating Consistent Pairwise Pseudo-Ranks—0
Optimizing Explanations by Network Canonization and Hyperparameter Search—0
PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals—0
Neuro-Symbolic Spatio-Temporal Reasoning—0
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images—0
A Short Survey of Systematic Generalization—0
Cross-Modal Contrastive Learning for Robust Reasoning in VQACode0
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference—0
A survey on knowledge-enhanced multimodal learning—0
CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering—0
Text-Aware Dual Routing Network for Visual Question Answering—0
AlignVE: Visual Entailment Recognition Based on Alignment Relations—0
Visually Grounded VQA by Lattice-based RetrievalCode0
Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQACode0
Learning to Answer Multilingual and Code-Mixed Questions—0
MF2-MVQA: A Multi-stage Feature Fusion method for Medical Visual Question Answering—0
Watching the News: Towards VideoQA Models that can Read—0
Towards Reasoning-Aware Explainable VQA—0
ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation—0
CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question AnsweringCode0
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question AnsweringCode0
What's Different between Visual Question Answering for Machine "Understanding" Versus for Accessibility?Code0
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems—0
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision—0
RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data—0
Image Semantic Relation Generation—0
Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering—0
Aligning MAGMA by Few-Shot Learning and Finetuning—0
Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero TrainingCode0
DCVQE: A Hierarchical Transformer for Video Quality Assessment—0
Multi-Modal Fusion Transformer for Visual Question Answering in Remote Sensing—0
HVS Revisited: A Comprehensive Video Quality Assessment Framework—0
MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning—0
Learning to Collocate Visual-Linguistic Neural Modules for Image CaptioningCode0
Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic ApproachCode0
On the Effects of Video Grounding on Language Models—0
A Dual-Attention Learning Network with Word and Sentence Embedding for Medical Visual Question AnsweringCode0
Dual Capsule Attention Mask Network with Mutual Learning for Visual Question Answering—0
Task Formulation Matters When Learning Continually: A Case Study in Visual Question AnsweringCode0
RepsNet: Combining Vision with Language for Automated Medical Reports—0
Exploring Modulated Detection Transformer as a Tool for Action Recognition in VideosCode0
Toward 3D Spatial Reasoning for Human-like Text-based Visual Question Answering—0
Continual VQA for Disaster Response SystemsCode0
Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar InstancesCode0
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks—0
LAVIS: A Library for Language-Vision Intelligence—0
MUST-VQA: MUltilingual Scene-text VQA—0
PaLI: A Jointly-Scaled Multilingual Language-Image Model—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
9InternVL-CAccuracy81.2—Unverified
10LyricsAccuracy81.2—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