Stylish Pieces: Advancing Deep Visual Style Recognition of Archaeological Fragments

Reconstructing fragmented archaeological artifacts is a central challenge in the study and preservation of ancient cultural heritage. A crucial step in this process often involves identifying the stylistic origin of isolated fragments, in part to dissociate fragments that could not originate from the same object. However, this task is inherently difficult: fragments often preserve only limited visual information, such as partial motifs or degraded surface details, and are frequently detached from their original archaeological context. Ancient Pompeian wall paintings, traditionally categorized into four styles, provide a compelling and historically significant case study. Over time, natural disasters and human activity caused many of the decorated structures to collapse, dispersing and mixing fragments from diverse sources. As a result, stylistic attribution has become a complex and labor-intensive task. In this work, we demonstrate how recent advances in deep image classification can be leveraged to develop highly accurate systems for fragment style recognition, addressing key challenges faced by archaeologists on site. To support this task, we introduce a new dataset for fragment-style classification based on the shapes of real fragments recovered from Pompeii excavations. Our research spans large-scale foundation models down to lightweight, edge-compatible networks, significantly improving on previous state-of-the-art methods while maintaining a balance between accuracy and computational efficiency.