Improve segmentation of occluded objects by using 2 layers to model occlusion with BCNet
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
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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