arXiv:2002.06264
공식 코드 · Papers with Code
Layered Embeddings for Amodal Instance Segmentation
원문 초록
The proposed method extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts. A fully convolutional network is trained to produce consistent pixel-level embedding across two layers such that, when clustered, the results convey the full spatial extent and depth ordering of each instance. Results demonstrate that the network can accurately estimate complete masks in the presence of occlusion and outperform leading top-down bounding-box approaches. Source code available at https://github.com/yanfengliu/layered_embeddings
함께 살펴볼 논문
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GIN: Generative INvariant Shape Prior for Amodal Instance Segmentation
OAFormer: Learning Occlusion Distinguishable Feature for Amodal Instance Segmentation
No time to train! Training-Free Reference-Based Instance Segmentation
NOCTIS: Novel Object Cyclic Threshold based Instance Segmentation
Prohibited Items Segmentation via Occlusion-aware Bilayer Modeling
The Four Color Theorem for Cell Instance Segmentation
토론
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