  Lithic Use-Wear Analysis (LUWA) using microscopic images is an underexplored
vision-for-science research area. It seeks to distinguish the worked material,
which is critical for understanding archaeological artifacts, material
interactions, tool functionalities, and dental records. However, this
challenging task goes beyond the well-studied image classification problem for
common objects. It is affected by many confounders owing to the complex wear
mechanism and microscopic imaging, which makes it difficult even for human
experts to identify the worked material successfully. In this paper, we
investigate the following three questions on this unique vision task for the
first time:(i) How well can state-of-the-art pre-trained models (like DINOv2)
generalize to the rarely seen domain? (ii) How can few-shot learning be
exploited for scarce microscopic images? (iii) How do the ambiguous
magnification and sensing modality influence the classification accuracy? To
study these, we collaborated with archaeologists and built the first
open-source and the largest LUWA dataset containing 23,130 microscopic images
with different magnifications and sensing modalities. Extensive experiments
show that existing pre-trained models notably outperform human experts but
still leave a large gap for improvements. Most importantly, the LUWA dataset
provides an underexplored opportunity for vision and learning communities and
complements existing image classification problems on common objects.
