Exploring Active Learning For Semiconductor Defect Segmentation

in this paper, we introduce a new video dataset for action segmentation, the BRIO-TA (BRIO Toy Assembly) dataset, which is designed to simulate operations in factory assembly. in contrast with existing datasets, BRIO-TA consists of two types of scenarios: normal work processes and anomalous work processes. Anomalies are further categorized into incorrect processes, omissions, and abnormal durations. The subjects in the videos are asked to perform either normal work or one of the three anomalies, and all video frames are manually annotated into 23 action classes. in addition, we propose a new metric called anomaly section accuracy (ASA) for evaluating the detection accuracy of anomalous segments in a video. With the new dataset and metric, we report that the state-of-the-art methods show a significantly low ASA, while they work for normal work segments. Demo videos are available at https://github.com/Tarmo-moriwaki/BRIO-TA_sample and the full dataset will be released after publication.