The reviewed record of science sign in
Pith

arxiv: 2408.04072 · v1 · pith:G747EF6M · submitted 2024-08-07 · cs.CV · cs.AI

AEye: A Visualization Tool for Image Datasets

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:G747EF6Mrecord.jsonopen to challenge →

classification cs.CV cs.AI
keywords datasetsaeyeimagehigh-dimensionalimagesmodelrepresentationssearch
0
0 comments X
read the original 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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Multimodal Data Curation Through Ranked Retrieval

    cs.IR 2026-05 unverdicted novelty 7.0

    Symmetric Nucleus Subsampling and Expert Embedding Engine reduce modality gaps in multimodal embeddings by over 90% and outperform baselines in data curation for downstream models.