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

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Showing 1001–1050 of 2167 papers

TitleStatusHype
HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Images—0
Champion Solution for the WSDM2023 Toloka VQA ChallengeCode3
Towards Models that Can See and Read—0
Curriculum Script Distillation for Multilingual Visual Question Answering—0
Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding TasksCode0
SlideVQA: A Dataset for Document Visual Question Answering on Multiple ImagesCode1
Multimodal Inverse Cloze Task for Knowledge-based Visual Question AnsweringCode1
Adaptively Clustering Neighbor Elements for Image-Text GenerationCode0
PromptCap: Prompt-Guided Image Captioning for VQA with GPT-3—0
Variational Causal Inference Network for Explanatory Visual Question AnsweringCode1
Toward Multi-Granularity Decision-Making: Explicit Visual Reasoning with Hierarchical KnowledgeCode0
Decouple Before Interact: Multi-Modal Prompt Learning for Continual Visual Question Answering—0
RMLVQA: A Margin Loss Approach for Visual Question Answering With Language Biases—0
From Images to Textual Prompts: Zero-Shot Visual Question Answering With Frozen Large Language Models—0
VQACL: A Novel Visual Question Answering Continual Learning SettingCode1
Dynamic Inference With Grounding Based Vision and Language Models—0
HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training—0
VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges—0
When are Lemons Purple? The Concept Association Bias of Vision-Language Models—0
From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language ModelsCode0
UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering—0
DePlot: One-shot visual language reasoning by plot-to-table translation—0
Towards Unsupervised Visual Reasoning: Do Off-The-Shelf Features Know How to Reason?—0
MIST: Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question AnsweringCode1
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering—0
SceneGATE: Scene-Graph based co-Attention networks for TExt visual question answering—0
CLIPPO: Image-and-Language Understanding from Pixels Only—0
REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge MemoryCode0
VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners—0
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language ExplanationsCode1
ParsVQA-Caps: A Benchmark for Visual Question Answering and Image Captioning in Persian—0
Hierarchical multimodal transformers for Multi-Page DocVQACode1
Review of Ansatz Designing Techniques for Variational Quantum Algorithms—0
InternVideo: General Video Foundation Models via Generative and Discriminative LearningCode4
Unifying Vision, Text, and Layout for Universal Document ProcessingCode3
Visual Question Answering From Another Perspective: CLEVR Mental Rotation TestsCode0
Compound Tokens: Channel Fusion for Vision-Language Representation Learning—0
Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual ReasoningCode1
Semi-supervised Learning of Perceptual Video Quality by Generating Consistent Pairwise Pseudo-Ranks—0
Optimizing Explanations by Network Canonization and Hyperparameter Search—0
PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals—0
Neuro-Symbolic Spatio-Temporal Reasoning—0
Seeing What You Miss: Vision-Language Pre-training with Semantic Completion LearningCode1
Self-supervised vision-language pretraining for Medical visual question answeringCode1
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images—0
A Short Survey of Systematic Generalization—0
X^2-VLM: All-In-One Pre-trained Model For Vision-Language TasksCode2
Cross-Modal Contrastive Learning for Robust Reasoning in VQACode0
Expectation-Maximization Contrastive Learning for Compact Video-and-Language RepresentationsCode1
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference—0
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All datasetsGQA Test2019VQA v2 test-devVQA v2 test-stdOK-VQAMSVD-QADocVQA testMSRVTT-QAInfographicVQAGQA test-devVizWiz 2020 VQAA-OKVQACLEVR

Benchmark Results

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