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Demonstration of MaskSearch: Efficiently Querying Image Masks for Machine Learning Workflows

arXiv 2404.06563 ↗First posted 9 Apr 2024cs.DB cs.LG cs.MMcited by 0

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Abstract (arXiv)

We demonstrate MaskSearch, a system designed to accelerate queries over databases of image masks generated by machine learning models. MaskSearch formalizes and accelerates a new category of queries for retrieving images and their corresponding masks based on mask properties, which support various applications, from identifying spurious correlations learned by models to exploring discrepancies between model saliency and human attention. This demonstration makes the following contributions:(1) the introduction of MaskSearch's graphical user interface (GUI), which enables interactive exploration of image databases through mask properties, (2) hands-on opportunities for users to explore MaskSearch's capabilities and constraints within machine learning workflows, and (3) an opportunity for conference attendees to understand how MaskSearch accelerates queries over image masks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 9 Apr 2024.

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References · 11

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  3. 2016Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?cited by 111
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  5. 2020Human Factors in Model Interpretability: Industry Practices, Challenges, and Needscited by 73
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  7. 2021Finding and Fixing Spurious Patterns with Explanationscited by 38
  8. 2021Human Attention in Fine-grained Classificationcited by 20
  9. 2020Detection Defense Against Adversarial Attacks with Saliency Mapcited by 10
  10. 2018DeepBase: Deep Inspection of Neural Networkscited by 3
  11. 2021DeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretationcited by 2

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