论文标题
分层的嵌入式用于Amodal实例分割
Layered Embeddings for Amodal Instance Segmentation
论文作者
论文摘要
所提出的方法通过明确包含可见零件和遮挡的零件来扩展语义实例分割的代表性输出。对完全卷积的网络进行了训练，以在两层上产生一致的像素级嵌入，因此，当聚类时，结果传达了每个实例的完整空间范围和深度顺序。结果表明，在遮挡和跑赢大盘前向下边界盒方法的情况下，网络可以准确估计完整的面具。源代码可从https://github.com/yanfengliu/layered_embeddings获得
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
