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 17011750 of 2167 papers

TitleStatusHype
Oscar: Object-Semantics Aligned Pre-training for Vision-Language TasksCode2
An Entropy Clustering Approach for Assessing Visual Question DifficultyCode0
YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific VideosCode1
Visual Grounding Methods for VQA are Working for the Wrong Reasons!Code1
Rephrasing visual questions by specifying the entropy of the answer distribution0
Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing0
Evaluating Multimodal Representations on Visual Semantic Textual SimilarityCode1
Generating Rationales in Visual Question Answering0
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal TransformersCode1
X-Linear Attention Networks for Image CaptioningCode1
Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene TextCode1
Assessing Image Quality Issues for Real-World Problems0
P NP, at least in Visual Question AnsweringCode0
Linguistically Driven Graph Capsule Network for Visual Question Reasoning0
Visual Question Answering for Cultural Heritage0
Normalized and Geometry-Aware Self-Attention Network for Image Captioning0
RSVQA: Visual Question Answering for Remote Sensing Data0
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAICode1
Counterfactual Samples Synthesizing for Robust Visual Question AnsweringCode1
MQA: Answering the Question via Robotic ManipulationCode0
PathVQA: 30000+ Questions for Medical Visual Question AnsweringCode1
Noise Estimation Using Density Estimation for Self-Supervised Multimodal LearningCode0
XGPT: Cross-modal Generative Pre-Training for Image Captioning0
A Question-Centric Model for Visual Question Answering in Medical ImagingCode0
A Study on Multimodal and Interactive Explanations for Visual Question Answering0
Visual Commonsense R-CNNCode1
Unshuffling Data for Improved Generalization0
Hierarchical Conditional Relation Networks for Video Question AnsweringCode1
A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment0
What BERT Sees: Cross-Modal Transfer for Visual Question Generation0
On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering0
VQA-LOL: Visual Question Answering under the Lens of Logic0
CQ-VQA: Visual Question Answering on Categorized Questions0
Sparse and Structured Visual AttentionCode0
Component Analysis for Visual Question Answering Architectures0
Multimodal fusion of imaging and genomics for lung cancer recurrence predictionCode1
Break It Down: A Question Understanding BenchmarkCode1
Augmenting Visual Question Answering with Semantic Frame Information in a Multitask Learning ApproachCode0
Uncertainty based Class Activation Maps for Visual Question Answering0
Robust Explanations for Visual Question AnsweringCode0
SQuINTing at VQA Models: Introspecting VQA Models with Sub-Questions0
Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models0
Recommending Themes for Ad Creative Design via Visual-Linguistic RepresentationsCode0
Extending Class Activation Mapping Using Gaussian Receptive Field0
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual FeaturesCode1
MHSAN: Multi-Head Self-Attention Network for Visual Semantic EmbeddingCode0
In Defense of Grid Features for Visual Question AnsweringCode1
Visual Question Answering on 360° Images0
Think Locally, Act Globally: Federated Learning with Local and Global RepresentationsCode1
Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering0
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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
9LyricsAccuracy81.2Unverified
10InternVL-CAccuracy81.2Unverified
#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