Improve segmentation of occluded objects by using 2 layers to model occlusion with BCNet
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Improve segmentation of occluded objects by using 2 layers to model occlusion with BCNet
Occlusion-Aware Instance Segmentation via BiLayer Network Architectures
arXiv paper abstract https://arxiv.org/abs/2208.04438v1
arXiv PDF paper https://arxiv.org/pdf/2208.04438v1.pdf
GitHub https://github.com/lkeab/BCNet
Segmenting highly-overlapping image objects is challenging, because there is typically no distinction between real object contours and occlusion boundaries on images.
… model image formation as a composition of two overlapping layers, and propose Bilayer Convolutional Network (BCNet), where the top layer detects occluding objects (occluders) and the bottom layer infers partially occluded instances (occludees).
… modeling of occlusion … decouples the boundaries of both the occluding and occluded instances, and considers the interaction between them during mask regression.
… formulate bilayer decoupling using the vision transformer (ViT), by representing instances in the image as separate learnable occluder and occludee queries.
Large … improvements using one/two-stage and query-based object detectors with various backbones and network layer choices validate the generalization ability of bilayer decoupling …
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