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
Question-Conditioned Counterfactual Image Generation for VQA0
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation0
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications0
Are we asking the right questions in MovieQA?0
Representing Movie Characters in Dialogues0
YouMakeup: A Large-Scale Domain-Specific Multimodal Dataset for Fine-Grained Semantic Comprehension0
TAB-VCR: Tags and Attributes based Visual Commonsense Reasoning BaselinesCode0
C3DVQA: Full-Reference Video Quality Assessment with 3D Convolutional Neural Network0
Learning Rich Image Region Representation for Visual Question Answering0
KnowIT VQA: Answering Knowledge-Based Questions about Videos0
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning0
Enforcing Reasoning in Visual Commonsense Reasoning0
Neural Memory Plasticity for Anomaly Detection0
Multi-modal Deep Analysis for Multimedia0
Modulated Self-attention Convolutional Network for VQA0
REMIND Your Neural Network to Prevent Catastrophic ForgettingCode0
From Strings to Things: Knowledge-Enabled VQA Model That Can Read and Reason0
SegEQA: Video Segmentation Based Visual Attention for Embodied Question Answering0
On Incorporating Semantic Prior Knowledge in Deep Learning Through Embedding-Space Constraints0
Compact Trilinear Interaction for Visual Question AnsweringCode0
On Incorporating Semantic Prior Knowlegde in Deep Learning Through Embedding-Space Constraints0
Learning to Recognize the Unseen Visual Predicates0
Why Does the VQA Model Answer No?: Improving Reasoning through Visual and Linguistic Inference0
UNITER: Learning UNiversal Image-TExt Representations0
Non-monotonic Logical Reasoning Guiding Deep Learning for Explainable Visual Question Answering0
Explainable High-order Visual Question Reasoning: A New Benchmark and Knowledge-routed Network0
Triplet-Aware Scene Graph Embeddings0
Learning Sparse Mixture of Experts for Visual Question Answering0
Inverse Visual Question Answering with Multi-Level Attentions0
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation0
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering0
PlotQA: Reasoning over Scientific Plots0
Adversarial Representation Learning for Text-to-Image Matching0
Visual Question Answering using Deep Learning: A Survey and Performance AnalysisCode0
U-CAM: Visual Explanation using Uncertainty based Class Activation Maps0
What is needed for simple spatial language capabilities in VQA?0
Language Features Matter: Effective Language Representations for Vision-Language Tasks0
Reactive Multi-Stage Feature Fusion for Multimodal Dialogue Modeling0
Fusion of Detected Objects in Text for Visual Question Answering0
Multimodal Unified Attention Networks for Vision-and-Language Interactions0
Why Does a Visual Question Have Different Answers?0
Multi-modality Latent Interaction Network for Visual Question Answering0
Question-Agnostic Attention for Visual Question Answering0
CRIC: A VQA Dataset for Compositional Reasoning on Vision and Commonsense0
Answering Questions about Data Visualizations using Efficient Bimodal FusionCode0
The Meaning of ``Most'' for Visual Question Answering Models0
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation0
LEAF-QA: Locate, Encode & Attend for Figure Question Answering0
An Empirical Study on Leveraging Scene Graphs for Visual Question Answering0
Bilinear Graph Networks 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