%0 Generic %1 qi2021openworld %A Qi, Lu %A Kuen, Jason %A Wang, Yi %A Gu, Jiuxiang %A Zhao, Hengshuang %A Lin, Zhe %A Torr, Philip %A Jia, Jiaya %D 2021 %K deeplearning open_world segmentation %T Open-World Entity Segmentation %U http://arxiv.org/abs/2107.14228 %X We introduce a new image segmentation task, termed Entity Segmentation (ES) with the aim to segment all visual entities in an image without considering semantic category labels. It has many practical applications in image manipulation/editing where the segmentation mask quality is typically crucial but category labels are less important. In this setting, all semantically-meaningful segments are equally treated as categoryless entities and there is no thing-stuff distinction. Based on our unified entity representation, we propose a center-based entity segmentation framework with two novel modules to improve mask quality. Experimentally, both our new task and framework demonstrate superior advantages as against existing work. In particular, ES enables the following: (1) merging multiple datasets to form a large training set without the need to resolve label conflicts; (2) any model trained on one dataset can generalize exceptionally well to other datasets with unseen domains. Our code is made publicly available at https://github.com/dvlab-research/Entity.

@misc{qi2021openworld, abstract = {We introduce a new image segmentation task, termed Entity Segmentation (ES) with the aim to segment all visual entities in an image without considering semantic category labels. It has many practical applications in image manipulation/editing where the segmentation mask quality is typically crucial but category labels are less important. In this setting, all semantically-meaningful segments are equally treated as categoryless entities and there is no thing-stuff distinction. Based on our unified entity representation, we propose a center-based entity segmentation framework with two novel modules to improve mask quality. Experimentally, both our new task and framework demonstrate superior advantages as against existing work. In particular, ES enables the following: (1) merging multiple datasets to form a large training set without the need to resolve label conflicts; (2) any model trained on one dataset can generalize exceptionally well to other datasets with unseen domains. Our code is made publicly available at https://github.com/dvlab-research/Entity.}, added-at = {2021-11-26T10:43:04.000+0100}, author = {Qi, Lu and Kuen, Jason and Wang, Yi and Gu, Jiuxiang and Zhao, Hengshuang and Lin, Zhe and Torr, Philip and Jia, Jiaya}, biburl = {https://www.bibsonomy.org/bibtex/22820a3e062440d43b1d35e92c000d37a/shuncheng.wu}, description = {https://arxiv.org/pdf/2107.14228.pdf}, interhash = {fb13ea196a23de633d01ffa973a1fc67}, intrahash = {2820a3e062440d43b1d35e92c000d37a}, keywords = {deeplearning open_world segmentation}, note = {cite arxiv:2107.14228Comment: Project page: http://luqi.info/Entity_Web}, timestamp = {2021-12-10T10:28:41.000+0100}, title = {Open-World Entity Segmentation}, url = {http://arxiv.org/abs/2107.14228}, year = 2021 }