mlsanityData to 14 Oct 2026

Simple Baseline for Visual Question Answering

arXiv 1512.02167 ↗First posted 7 Dec 2015cs.CV cs.CLcited by 24

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Abstract (arXiv)

We describe a very simple bag-of-words baseline for visual question answering. This baseline concatenates the word features from the question and CNN features from the image to predict the answer. When evaluated on the challenging VQA dataset [2], it shows comparable performance to many recent approaches using recurrent neural networks. To explore the strength and weakness of the trained model, we also provide an interactive web demo and open-source code. .

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Dec 2015.

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Citing papers per year: 2021: 7; 2022: 8; 2023: 6; 2024: 3.

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  1. 2022Clotho-AQA: A Crowdsourced Dataset for Audio Question Answeringcited by 85
  2. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 76
  3. 2021Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answeringcited by 62
  4. 2022SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answeringcited by 38
  5. 2022Towards Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong Baselinecited by 25
  6. 2023From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunitiescited by 25
  7. 2021ProTo: Program-Guided Transformer for Program-Guided Taskscited by 21
  8. 2024LOVA3: Learning to Visual Question Answering, Asking and Assessmentcited by 21
  9. 2021VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysiscited by 15
  10. 2022VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challengescited by 14
  11. 2024Exploring Phrase-Level Grounding with Text-to-Image Diffusion Modelcited by 13
  12. 2022Experimental Standards for Deep Learning in Natural Language Processing Researchcited by 10
12 more
  1. 2021MuVAM: A Multi-View Attention-based Model for Medical Visual Question Answeringcited by 4
  2. 2023EAML: Ensemble Self-Attention-based Mutual Learning Network for Document Image Classificationcited by 4
  3. 2023Semi-Supervised Panoptic Narrative Groundingcited by 4
  4. 2021Show Why the Answer is Correct! Towards Explainable AI using Compositional Temporal Attentioncited by 2
  5. 2022MGA-VQA: Multi-Granularity Alignment for Visual Question Answeringcited by 2
  6. 2021A Better Loss for Visual-Textual Groundingcited by 1
  7. 2022Bilaterally Slimmable Transformer for Elastic and Efficient Visual Question Answeringcited by 1
  8. 2023BinaryVQA: A Versatile Test Set to Evaluate the Out-of-Distribution Generalization of VQA Modelscited by 1
  9. 2023Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal Imagerycited by 1
  10. 2021Will Multi-modal Data Improves Few-shot Learning?cited by 0
  11. 2022Deep Neural Networks for Visual Reasoningcited by 0
  12. 2023Bridge Damage Cause Estimation Using Multiple Images Based on Visual Question Answeringcited by 0