An unsupervised pipeline for class-agnostic object detection using self-supervised vision transformers and Kolmogorov–Arnold Networks

Object detection typically requires costly manual bounding box annotations. We propose a fully unsupervised four-stage pipeline that automatically generates pseudo-bounding boxes: (1) Background Removal, (2) Pseudo-Bounding Box Generation, (3) Complementary Bounding Box Generation (optional), and (4...

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Detalhes bibliográficos

Principais autores:	Yuno Otsuka, Pittipol Kantavat, Boonserm Kijsirikul
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Elsevier 2026-06-01
coleção:	Machine Learning with Applications
Assuntos:	Class-agnostic object detection Kolmogorov–Arnold Networks Self-supervised learning Unsupervised bounding box generation Unsupervised object localization Vision transformers
Acesso em linha:	http://www.sciencedirect.com/science/article/pii/S2666827026000356
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