MilInst: Enhanced Instance Segmentation Framework for Military Camouflaged Targets Using Sparse Instance Activation
In this study, an improved end-to-end framework for instance segmentation of military camouflaged targets, referred to as MilInst, is proposed. The framework builds upon SparseInst method developed by Cheng et al. (2022). Several improvements are introduced to enhance the model’s performa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10261198/ |