SangWook Yoo

DaeEun Yoon, Semin Kim, SangWook Yoo et al.

In recent years, there has been tremendous progress in object detection performance. However, despite these advances, the detection performance for small objects is significantly inferior to that of large objects. Detecting small objects is one of the most challenging and important problems in computer vision. To improve the detection performance for small objects, we propose an optimal data augmentation method using Fast AutoAugment. Through our proposed method, we can quickly find optimal augmentation policies that can overcome degradation when detecting small objects, and we achieve a 20% performance improvement on the DOTA dataset.

Shohrukh Bekmirzaev, Seoyoung Oh, Sangwook Yoo

Semantic segmentation is a hot topic in computer vision where the most challenging tasks of object detection and recognition have been handling by the success of semantic segmentation approaches. We propose a concept of object-by-object learning technique to detect 11 types of facial skin lesions using semantic segmentation methods. Detecting individual skin lesion in a dense group is a challenging task, because of ambiguities in the appearance of the visual data. We observe that there exist co-occurrent visual relations between object classes (e.g., wrinkle and age spot, or papule and whitehead, etc.). In fact, rich contextual information significantly helps to handle the issue. Therefore, we propose REthinker blocks that are composed of the locally constructed convLSTM/Conv3D layers and SE module as a one-shot attention mechanism whose responsibility is to increase network's sensitivity in the local and global contextual representation that supports to capture ambiguously appeared objects and co-occurrence interactions between object classes. Experiments show that our proposed model reached MIoU of 79.46% on the test of a prepared dataset, representing a 15.34% improvement over Deeplab v3+ (MIoU of 64.12%).