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论文类型： 期刊论文

发表时间： 2020-01-01

发表刊物： IEEE TRANSACTIONS ON IMAGE PROCESSING

收录刊物： EI、SCIE

卷号： 29

页面范围： 1356-1367

ISSN号： 1057-7149

关键字： Training; Task analysis; Object segmentation; Semantics; Image segmentation; Dogs; Focus region detection; recurrent constraint network; fully convolutional network; box-level supervision

摘要： Recent state-of-the-art methods on focus region detection (FRD) rely on deep convolutional networks trained with costly pixel-level annotations. In this study, we propose a FRD method that achieves competitive accuracies but only uses easily obtained bounding box annotations. Box-level tags provide important cues of focus regions but lose the boundary delineation of the transition area. A recurrent constraint network (RCN) is introduced for this challenge. In our static training, RCN is jointly trained with a fully convolutional network (FCN) through box-level supervision. The RCN can generate a detailed focus map to locate the boundary of the transition area effectively. In our dynamic training, we iterate between fine-tuning FCN and RCN with the generated pixel-level tags and generate finer new pixel-level tags. To boost the performance further, a guided conditional random field is developed to improve the quality of the generated pixel-level tags. To promote further study of the weakly supervised FRD methods, we construct a new dataset called FocusBox, which consists of 5000 challenging images with bounding box-level labels. Experimental results on existing datasets demonstrate that our method not only yields comparable results than fully supervised counterparts but also achieves a faster speed.