mlsanityData to 7 Oct 2026

AEye: A Visualization Tool for Image Datasets

arXiv 2408.04072 ↗First posted 7 Aug 2024cs.CV cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 7 Aug 2024.

Builds on

9 of 21 references.

References · 9

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2023Visual Instruction Tuningcited by 7,792
  3. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  4. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  5. 2020REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasetscited by 136
  6. 2021A Survey on Bias in Visual Datasetscited by 84
  7. 2016Embedding Projector: Interactive Visualization and Interpretation of Embeddingscited by 43
  8. 2022Unsupervised visualization of image datasets using contrastive learningcited by 16
  9. 2024Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformationcited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.