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 15511600 of 2167 papers

TitleStatusHype
Unshuffling Data for Improved Generalization in Visual Question Answering0
Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering0
Erasure for Advancing: Dynamic Self-Supervised Learning for Commonsense Reasoning0
Differentiable End-to-End Program Executor for Sample and Computationally Efficient VQA0
Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings0
Detecting Hate Speech in Multi-modal MemesCode1
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document UnderstandingCode0
Learning content and context with language bias for Visual Question AnsweringCode0
Object-Centric Diagnosis of Visual Reasoning0
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA0
Trying Bilinear Pooling in Video-QA0
On Modality Bias in the TVQA DatasetCode0
Overcoming Language Priors with Self-supervised Learning for Visual Question AnsweringCode1
Knowledge-Routed Visual Question Reasoning: Challenges for Deep Representation EmbeddingCode1
KVL-BERT: Knowledge Enhanced Visual-and-Linguistic BERT for Visual Commonsense Reasoning0
Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps0
Study on the Assessment of the Quality of Experience of Streaming VideoCode0
TAP: Text-Aware Pre-training for Text-VQA and Text-CaptionCode1
CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractionsCode1
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene UnderstandingCode1
WeaQA: Weak Supervision via Captions for Visual Question Answering0
Understanding Guided Image Captioning Performance across DomainsCode0
Towards Knowledge-Augmented Visual Question AnsweringCode0
A Unified Framework for Multilingual and Code-Mixed Visual Question Answering0
Open-Ended Multi-Modal Relational Reasoning for Video Question AnsweringCode0
Just Ask: Learning to Answer Questions from Millions of Narrated VideosCode1
Multimodal Graph Networks for Compositional Generalization in Visual Question Answering0
Patch-VQ: 'Patching Up' the Video Quality ProblemCode1
Point and Ask: Incorporating Pointing into Visual Question AnsweringCode1
Learning from Lexical Perturbations for Consistent Visual Question AnsweringCode0
Transformation Driven Visual ReasoningCode1
Siamese Tracking with Lingual Object ConstraintsCode0
Large Scale Multimodal Classification Using an Ensemble of Transformer Models and Co-AttentionCode1
Interpretable Visual Reasoning via Induced Symbolic SpaceCode0
Modular Graph Attention Network for Complex Visual Relational Reasoning0
LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question AnsweringCode1
Logically Consistent Loss for Visual Question Answering0
Generating Natural Questions from Images for Multimodal Assistants0
CapWAP: Captioning with a Purpose0
Learning to Model and Ignore Dataset Bias with Mixed Capacity EnsemblesCode0
Disentangling 3D Prototypical Networks For Few-Shot Concept LearningCode1
An Improved Attention for Visual Question AnsweringCode0
Reasoning Over History: Context Aware Visual Dialog0
Can Pre-training help VQA with Lexical Variations?0
Representation, Learning and Reasoning on Spatial Language for Downstream NLP Tasks0
ConceptBert: Concept-Aware Representation for Visual Question AnsweringCode1
ISAAQ - Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention0
CapWAP: Image Captioning with a Purpose0
Learning to Contrast the Counterfactual Samples for Robust Visual Question AnsweringCode1
STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering0
Show:102550
← PrevPage 32 of 44Next →

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