AEye: A Visualization Tool for Image Datasets
Florian Grötschla - ETH Zurich, Zurich, Switzerland
Luca A Lanzendörfer - ETH Zurich, Zurich, Switzerland
Marco Calzavara - ETH Zurich, Zurich, Switzerland
Roger Wattenhofer - ETH Zurich, Zurich, Switzerland
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Room: Bayshore VI
2024-10-17T15:09:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T15:09:00Z
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Keywords
Image embeddings, image visualization, contrastive learning, semantic search.
Abstract
Image datasets serve as the foundation for machine learning models in computer vision, significantly influencing model capabilities, performance, and biases alongside architectural considerations. Therefore, understanding the composition and distribution of these datasets has become increasingly crucial. To address the need for intuitive exploration of these datasets, we propose AEye, an extensible and scalable visualization tool tailored to image datasets. AEye utilizes a contrastively trained model to embed images into semantically meaningful high-dimensional representations, facilitating data clustering and organization. To visualize the high-dimensional representations, we project them onto a two-dimensional plane and arrange images in layers so users can seamlessly navigate and explore them interactively. AEye facilitates semantic search functionalities for both text and image queries, enabling users to search for content. We open-source the codebase for AEye, and provide a simple configuration to add datasets.