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 17511800 of 2167 papers

TitleStatusHype
Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits0
Vision and Language: from Visual Perception to Content Creation0
Deep Exemplar Networks for VQA and VQG0
KonVid-150k: A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild0
Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing0
AI2D-RST: A multimodal corpus of 1000 primary school science diagrams0
Weak Supervision helps Emergence of Word-Object Alignment and improves Vision-Language Tasks0
12-in-1: Multi-Task Vision and Language Representation LearningCode0
Deep Bayesian Active Learning for Multiple Correct Outputs0
TAB-VCR: Tags and Attributes based VCR BaselinesCode0
RUBi: Reducing Unimodal Biases for Visual Question AnsweringCode0
A Free Lunch in Generating Datasets: Building a VQG and VQA System with Attention and Humans in the Loop0
Assessing the Robustness of Visual Question Answering Models0
OptiBox: Breaking the Limits of Proposals for Visual Grounding0
Transfer Learning in Visual and Relational Reasoning0
Unsupervised Keyword Extraction for Full-sentence VQA0
Temporal Reasoning via Audio Question AnsweringCode0
Explanation vs Attention: A Two-Player Game to Obtain Attention for VQA0
DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual DialogueCode0
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
YouMakeup: A Large-Scale Domain-Specific Multimodal Dataset for Fine-Grained Semantic Comprehension0
Representing Movie Characters in Dialogues0
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
Enforcing Reasoning in Visual Commonsense Reasoning0
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning0
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
SegEQA: Video Segmentation Based Visual Attention for Embodied Question Answering0
From Strings to Things: Knowledge-Enabled VQA Model That Can Read and Reason0
On Incorporating Semantic Prior Knowledge in Deep Learning Through Embedding-Space Constraints0
Compact Trilinear Interaction for Visual Question AnsweringCode0
Overcoming Data Limitation in Medical Visual Question AnsweringCode1
UNITER: Learning UNiversal Image-TExt Representations0
Learning to Recognize the Unseen Visual Predicates0
Why Does the VQA Model Answer No?: Improving Reasoning through Visual and Linguistic Inference0
On Incorporating Semantic Prior Knowlegde in Deep Learning Through Embedding-Space Constraints0
UNITER: UNiversal Image-TExt Representation LearningCode1
Unified Vision-Language Pre-Training for Image Captioning and VQACode2
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
Learning Sparse Mixture of Experts for Visual Question Answering0
Triplet-Aware Scene Graph Embeddings0
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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