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

Showing 18011850 of 2167 papers

TitleStatusHype
Object Attribute Matters in Visual Question AnsweringCode0
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and ResultsCode0
Weakly Supervised Relative Spatial Reasoning for Visual Question AnsweringCode0
EaSe: A Diagnostic Tool for VQA based on Answer DiversityCode0
OG-SGG: Ontology-Guided Scene Graph Generation. A Case Study in Transfer Learning for Telepresence RoboticsCode0
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring ExpressionsCode0
MaMMUT: A Simple Architecture for Joint Learning for MultiModal TasksCode0
SURE-VQA: Systematic Understanding of Robustness Evaluation in Medical VQA TasksCode0
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question AnsweringCode0
OmniFusion Technical ReportCode0
M^2ConceptBase: A Fine-Grained Aligned Concept-Centric Multimodal Knowledge BaseCode0
OmniNet: A unified architecture for multi-modal multi-task learningCode0
Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal ImageryCode0
AdaVQA: Overcoming Language Priors with Adapted Margin Cosine LossCode0
CLEVR\_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over ImagesCode0
LXMERT Model Compression for Visual Question AnsweringCode0
Dynamic Memory Networks for Visual and Textual Question AnsweringCode0
Dynamic Key-value Memory Enhanced Multi-step Graph Reasoning for Knowledge-based Visual Question AnsweringCode0
DVQA: Understanding Data Visualizations via Question AnsweringCode0
Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic ApproachCode0
On Modality Bias in the TVQA DatasetCode0
On Modality Bias Recognition and ReductionCode0
Variational Quantum Optimization with Continuous BanditsCode0
Targeted Visual Prompting for Medical Visual Question AnsweringCode0
DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual DialogueCode0
CLEVR_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over ImagesCode0
Dual Recurrent Attention Units for Visual Question AnsweringCode0
LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question AnsweringCode0
Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance ViewCode0
Dual Attention Networks for Visual Reference Resolution in Visual DialogCode0
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question AnsweringCode0
A Dataset and Architecture for Visual Reasoning with a Working MemoryCode0
CLEAR: A Dataset for Compositional Language and Elementary Acoustic ReasoningCode0
Looking Beyond Visible Cues: Implicit Video Question Answering via Dual-Clue ReasoningCode0
Logical Implications for Visual Question Answering ConsistencyCode0
Locally Smoothed Neural NetworksCode0
LMM-VQA: Advancing Video Quality Assessment with Large Multimodal ModelsCode0
Open-Ended Multi-Modal Relational Reasoning for Video Question AnsweringCode0
Open-Ended Visual Question-AnsweringCode0
Synthetic Document Question Answering in HungarianCode0
LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic SurgeryCode0
LLaVA-OneVision: Easy Visual Task TransferCode0
Open-Set Knowledge-Based Visual Question Answering with Inference PathsCode0
OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in VietnameseCode0
LININ: Logic Integrated Neural Inference Network for Explanatory Visual Question AnsweringCode0
Systematic Generalization: What Is Required and Can It Be Learned?Code0
Optimal training of variational quantum algorithms without barren plateausCode0
CAST: Cross-modal Alignment Similarity Test for Vision Language ModelsCode0
T2I-FineEval: Fine-Grained Compositional Metric for Text-to-Image EvaluationCode0
Dual Attention Networks for Multimodal Reasoning and MatchingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1humanAccuracy89.3Unverified
2DREAM+Unicoder-VL (MSRA)Accuracy76.04Unverified
3TRRNet (Ensemble)Accuracy74.03Unverified
4MIL-nbgaoAccuracy73.81Unverified
5Kakao BrainAccuracy73.33Unverified
6Coarse-to-Fine Reasoning, Single ModelAccuracy72.14Unverified
7270Accuracy70.23Unverified
8NSM ensemble (updated)Accuracy67.55Unverified
9VinVL-DPTAccuracy64.92Unverified
10VinVL+LAccuracy64.85Unverified
#ModelMetricClaimedVerifiedStatus
1PaLIAccuracy84.3Unverified
2BEiT-3Accuracy84.19Unverified
3VLMoAccuracy82.78Unverified
4ONE-PEACEAccuracy82.6Unverified
5mPLUG (Huge)Accuracy82.43Unverified
6CuMo-7BAccuracy82.2Unverified
7X2-VLM (large)Accuracy81.9Unverified
8MMUAccuracy81.26Unverified
9InternVL-CAccuracy81.2Unverified
10LyricsAccuracy81.2Unverified
#ModelMetricClaimedVerifiedStatus
1BEiT-3overall84.03Unverified
2mPLUG-Hugeoverall83.62Unverified
3ONE-PEACEoverall82.52Unverified
4X2-VLM (large)overall81.8Unverified
5VLMooverall81.3Unverified
6SimVLMoverall80.34Unverified
7X2-VLM (base)overall80.2Unverified
8VASToverall80.19Unverified
9VALORoverall78.62Unverified
10Prompt Tuningoverall78.53Unverified