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 10011025 of 2167 papers

TitleStatusHype
HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Images0
Champion Solution for the WSDM2023 Toloka VQA ChallengeCode3
Towards Models that Can See and Read0
Curriculum Script Distillation for Multilingual Visual Question Answering0
SlideVQA: A Dataset for Document Visual Question Answering on Multiple ImagesCode1
Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding TasksCode0
Multimodal Inverse Cloze Task for Knowledge-based Visual Question AnsweringCode1
Adaptively Clustering Neighbor Elements for Image-Text GenerationCode0
Variational Causal Inference Network for Explanatory Visual Question AnsweringCode1
PromptCap: Prompt-Guided Image Captioning for VQA with GPT-30
Decouple Before Interact: Multi-Modal Prompt Learning for Continual Visual Question Answering0
Toward Multi-Granularity Decision-Making: Explicit Visual Reasoning with Hierarchical KnowledgeCode0
RMLVQA: A Margin Loss Approach for Visual Question Answering With Language Biases0
VQACL: A Novel Visual Question Answering Continual Learning SettingCode1
From Images to Textual Prompts: Zero-Shot Visual Question Answering With Frozen Large Language Models0
Dynamic Inference With Grounding Based Vision and Language Models0
HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training0
VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges0
When are Lemons Purple? The Concept Association Bias of Vision-Language Models0
UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering0
From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language ModelsCode0
Towards Unsupervised Visual Reasoning: Do Off-The-Shelf Features Know How to Reason?0
DePlot: One-shot visual language reasoning by plot-to-table translation0
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering0
MIST: Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question AnsweringCode1
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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