Matteo Fusconi, Alex Costanzino, Pierluigi Zama Ramirez, Luigi Di Stefano · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31644 · open-access PDF
As Representações Aprendizáveis (LRs) podem combinar famílias paramétricas de funções com diversos tipos de sinais. Estes formatos de dados neural-nativos emergentes estão reunindo interesse na comunidade científica devido à sua compacidade, agnosticismo de resolução e amenidade para o processamento neural direto. Por outro lado, as imagens de gigapixels estão se tornando cada vez mais difundidas em uma variedade de domínios científicos, culturais e industriais. Neste artigo, apresentamos o GigaBench, o primeiro benchmark e avaliação experimental focado na implantação de LRs com imagens de gigapixels.
Machine translation (Meta M2M100); the English original follows.
Learnable Representations (LRs) can fit parametric families of functions to diverse kinds of signals. These emergent neural-native data formats are gathering interest in the scientific community due to their compactness, resolution-agnosticism and amenability to direct neural processing. On the other hand, gigapixel images are becoming increasingly widespread across a variety of scientific, cultural, and industrial domains. In this paper, we present GigaBench, the first benchmark and experimental evaluation focused on deploying LRs with gigapixel imagery. GigaBench includes images collected, a
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