Zhongbin Sun

Zhongbin Sun, Xiaolong Li, Yiran Li et al.

Unsupervised anomaly detection is a challenging computer vision task, in which 2D-based anomaly detection methods have been extensively studied. However, multimodal anomaly detection based on RGB images and 3D point clouds requires further investigation. The existing methods are mainly inspired by memory bank based methods commonly used in 2D-based anomaly detection, which may cost extra memory for storing mutimodal features. In present study, a novel memoryless method MDSS is proposed for multimodal anomaly detection, which employs a light-weighted student-teacher network and a signed distance function to learn from RGB images and 3D point clouds respectively, and complements the anomaly information from the two modalities. Specifically, a student-teacher network is trained with normal RGB images and masks generated from point clouds by a dynamic loss, and the anomaly score map could be obtained from the discrepancy between the output of student and teacher. Furthermore, the signed distance function learns from normal point clouds to predict the signed distances between points and surface, and the obtained signed distances are used to generate anomaly score map. Subsequently, the anomaly score maps are aligned to generate the final anomaly score map for detection. The experimental results indicate that MDSS is comparable but more stable than the SOTA memory bank based method Shape-guided, and furthermore performs better than other baseline methods.

Zhongbin Sun, Junqi Li, Heli Sun

Background: Two public defect data, including Jureczko and NASA datasets, have been widely used in cross project defect prediction (CPDP). The quality of defect data have been reported as an important factor influencing the defect prediction performance and Shepperd et al. have researched the data quality problems in NASA datasets. However, up to now, there is no research focusing on the quality problems of Jureczko datasets which are most widely used in CPDP. Aims: In this paper, we intend to investigate the problems of identical and inconsistent cases in Jureczko datasets and validate whether removing these problematic cases will make a difference to defect prediction performance in CPDP. Method: The problems of identical and inconsistent cases are reported from two aspects, respectively in each individual dataset and in a pair of datasets from different releases of a software project. Then a cleaned version of Jureczko datasets is provided by removing duplicate and inconsistent cases. Finally three training data selection methods are employed to compare the defect prediction performance of cleaned datasets with that of original datasets. Results: The experimental results in terms of AUC and F-Measure show that most datasets obtain very different defect prediction performance. Conclusions: It is very necessary to study the data quality problems in CPDP and the cleaned Jureczko datasets may provide more reliable defect prediction performance in CPDP.