  Vision-based industrial inspection (VII) aims to locate defects quickly and
accurately. Supervised learning under a close-set setting and industrial
anomaly detection, as two common paradigms in VII, face different problems in
practical applications. The former is that various and sufficient defects are
difficult to obtain, while the latter is that specific defects cannot be
located. To solve these problems, in this paper, we focus on the few-shot
semantic segmentation (FSS) method, which can locate unseen defects conditioned
on a few annotations without retraining. Compared to common objects in natural
images, the defects in VII are small. This brings two problems to current FSS
methods: 1 distortion of target semantics and 2 many false positives for
backgrounds. To alleviate these problems, we propose a small object few-shot
segmentation (SOFS) model. The key idea for alleviating 1 is to avoid the
resizing of the original image and correctly indicate the intensity of target
semantics. SOFS achieves this idea via the non-resizing procedure and the
prototype intensity downsampling of support annotations. To alleviate 2, we
design an abnormal prior map in SOFS to guide the model to reduce false
positives and propose a mixed normal Dice loss to preferentially prevent the
model from predicting false positives. SOFS can achieve FSS and few-shot
anomaly detection determined by support masks. Diverse experiments substantiate
the superior performance of SOFS. Code is available at
https://github.com/zhangzilongc/SOFS.
