Box2Mask: New Box-Supervised Video Instance Segmentation Method
The approach might make it easier to replace parts of a video.
Researchers have presented a new single-shot instance segmentation approach Box2Mask, which can detect objects in a video by "taking advantage of simple box annotations". It "integrates the classical level-set evolution model into deep neural network learning to achieve accurate mask prediction with only bounding box supervision."
Box2Mask can "figure out" local context and spatial relations thanks to its local consistency module based on a pixel affinity kernel. The researchers developed two types of single-stage frameworks to empower the level-set evolution for box-supervised instance segmentation. Each framework has an instance-aware decoder, box-level matching assignment, and level-set evolution. The mask map of each instance can be iteratively optimized in its bounding box annotation.
The creators claim their method provides a highly accurate result and narrows the "performance gap between the fully mask-supervised and box-supervised approach." Practically, it could make replacing objects in a video much easier and cleaner.
If you'd like to learn more about the approach, read the paper here and find the code on GitHub. Also, don't forget to join our 80 Level Talent platform, our Reddit page, and our Telegram channel, follow us on Instagram and Twitter, where we share breakdowns, the latest news, awesome artworks, and more.
