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Segment unknown objects using transformers by stopping the gradient using SWORD

Segment unknown objects using transformers by stopping the gradient using SWORD


Exploring Transformers for Open-world Instance Segmentation

arXiv paper abstract https://arxiv.org/abs/2308.04206



Open-world instance segmentation is a rising task, which aims to segment all objects in the image by learning from a limited number of base-category objects.


... utilize the Transformer for open-world instance segmentation and present SWORD ... introduce to attach the stop-gradient operation before classification head and further add IoU heads for discovering novel objects.


... demonstrate that a simple stop-gradient operation not only prevents the novel objects from being suppressed as background, but also allows the network to enjoy the merit of heuristic label assignment.


... propose a novel contrastive learning framework to enlarge the representations between objects and background.


... maintain a universal object queue to obtain the object center, and dynamically select positive and negative samples from the object queries for contrastive learning.


... models achieve state-of-the-art performance in various open-world cross-category and cross-dataset generalizations ...



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