mlsanityData to 14 Oct 2026

Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering

arXiv 1704.03162 ↗First posted 11 Apr 2017cs.CVcited by 31

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Abstract (arXiv)

This paper presents a new baseline for visual question answering task. Given an image and a question in natural language, our model produces accurate answers according to the content of the image. Our model, while being architecturally simple and relatively small in terms of trainable parameters, sets a new state of the art on both unbalanced and balanced VQA benchmark. On VQA 1.0 open ended challenge, our model achieves 64.6% accuracy on the test-standard set without using additional data, an improvement of 0.4% over state of the art, and on newly released VQA 2.0, our model scores 59.7% on validation set outperforming best previously reported results by 0.5%. The results presented in this paper are especially interesting because very similar models have been tried before but significantly lower performance were reported. In light of the new results we hope to see more meaningful research on visual question answering in the future.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Apr 2017.

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Citing papers per year: 2021: 9; 2022: 6; 2023: 9; 2024: 7.

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  1. 2021InfographicVQAcited by 437
  2. 2022Revisiting Weakly Supervised Pre-Training of Visual Perception Modelscited by 116
  3. 2022MapQA: A Dataset for Question Answering on Choropleth Mapscited by 85
  4. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 76
  5. 2021Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answeringcited by 71
  6. 2023Learnable Cross-modal Knowledge Distillation for Multi-modal Learning with Missing Modalitycited by 45
  7. 2024EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Samplingcited by 45
  8. 2022Rethinking Attention-Model Explainability through Faithfulness Violation Testcited by 43
  9. 2023Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directionscited by 25
  10. 2023From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunitiescited by 25
  11. 2023OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamesecited by 20
  12. 2021MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Groundingcited by 19
19 more
  1. 2021AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Losscited by 18
  2. 2021Robustness through Data Augmentation Loss Consistencycited by 8
  3. 2024ViOCRVQA: Novel Benchmark Dataset and Vision Reader for Visual Question Answering by Understanding Vietnamese Text in Imagescited by 8
  4. 2022Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answeringcited by 7
  5. 2024AdaCM2: On Understanding Extremely Long-Term Video with Adaptive Cross-Modality Memory Reductioncited by 7
  6. 2022Towards Answering Open-ended Ethical Quandary Questionscited by 3
  7. 2023PAT: Parallel Attention Transformer for Visual Question Answering in Vietnamesecited by 3
  8. 2024Story Generation from Visual Inputs: Techniques, Related Tasks, and Challengescited by 3
  9. 2021Latent Variable Models for Visual Question Answeringcited by 2
  10. 2021Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Modelscited by 2
  11. 2021Show Why the Answer is Correct! Towards Explainable AI using Compositional Temporal Attentioncited by 2
  12. 2021Multi-Image Visual Question Answeringcited by 2
  13. 2023Medical visual question answering using joint self-supervised learningcited by 2
  14. 2024@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technologycited by 2
  15. 2022Can you even tell left from right? Presenting a new challenge for VQAcited by 0
  16. 2023VQA with Cascade of Self- and Co-Attention Blockscited by 0
  17. 2023Knowledge-Based Counterfactual Queries for Visual Question Answeringcited by 0
  18. 2023On the Cognition of Visual Question Answering Models and Human Intelligence: A Comparative Studycited by 0
  19. 2024Enhanced Textual Feature Extraction for Visual Question Answering: A Simple Convolutional Approachcited by 0