Weakly-Supervised Contrastive Learning for Unsupervised Object Discovery.

Unsupervised object discovery (UOD) refers to the task of discriminating the whole region of objects from the background within a scene without relying on labeled datasets, which benefits the task of bounding-box-level localization and pixel-level segmentation. This task is promising due to its ability to discover objects in a generic manner. We roughly categorize existing techniques into two main directions, namely the generative solutions based on image resynthesis, and the clustering methods based on self-supervised models.

FRD-CNN: Object detection based on small-scale convolutional neural networks and feature reuse.

Most of the recent successful object detection methods have been based on convolutional neural networks (CNNs). From previous studies, we learned that many feature reuse methods improve the network performance, but they increase the number of parameters. DenseNet uses thin layers that have fewer channels to alleviate the increase in parameters.