Matteo Fusconi, Alex Costanzino, Pierluigi Zama Ramirez, Luigi Di Stefano · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31644 · open-access PDF
Learnable Representations (LRs) 可以将参数组合的函数组合合到各种信号中。这些新兴的神经原生数据格式因其紧凑性、分辨率敏感性和直接神经处理能力而引起科学界的关注。另一方面,千兆像素图像在各种科学、文化和工业领域中日益普及。在本文中,我们介绍了 GigaBench,这是以千兆像素图像为中心的首次基准和实验性评估。
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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