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Weakly-supervised instance segmentation, which could greatly save labor and time cost of pixel mask annotation, has attracted increasing attention in recent years. The commonly used pipeline firstly utilizes conventional image segmentation methods to automatically generate initial masks and then use them to train an off-the-shelf segmentation network in an iterative way. However, the initial generated masks usually contains a notable proportion of invalid masks which are mainly caused by small object instances. Directly using these initial masks to train segmentation model is harmful for the performance. To address this problem, we propose a hybrid network in this paper. In our architecture, there is a principle segmentation network which is used to handle the normal samples with valid generated masks. In addition, a complementary branch is added to handle the small and dim objects without valid masks. Experimental results indicate that our method can achieve significantly performance improvement both on the small object instances and large ones, and outperforms all state-of-the-art methods.
We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks. This task is formulated as a combination of weakly supervised object detection and semantic segmentation, where individual objects of
Currently, instance segmentation is attracting more and more attention in machine learning region. However, there exists some defects on the information propagation in previous Mask R-CNN and other network models. In this paper, we propose supervised
Weakly supervised instance segmentation reduces the cost of annotations required to train models. However, existing approaches which rely only on image-level class labels predominantly suffer from errors due to (a) partial segmentation of objects and
We propose point-based instance-level annotation, a new form of weak supervision for instance segmentation. It combines the standard bounding box annotation with labeled points that are uniformly sampled inside each bounding box. We show that the exi
We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision. Specifically, we propose a self-ensembling framework where instance segmentation and semantic correspondenc