Supervized segmentation with graph-structured deep metric learning
| Autor: | Loic Landrieu, Mohamed Boussaha |
|---|---|
| Přispěvatelé: | Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), Landrieu, Loic |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Statistics - Machine Learning Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] Machine Learning (stat.ML) [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] [STAT.ML] Statistics [stat]/Machine Learning [stat.ML] Machine Learning (cs.LG) |
| Zdroj: | ICML Workshop on Learning and Reasoning with Graph-Structured Representations ICML Workshop on Learning and Reasoning with Graph-Structured Representations, Jun 2019, Long Beach (CA), United States HAL |
| Popis: | We present a fully-supervized method for learning to segment data structured by an adjacency graph. We introduce the graph-structured contrastive loss, a loss function structured by a ground truth segmentation. It promotes learning vertex embeddings which are homogeneous within desired segments, and have high contrast at their interface. Thus, computing a piecewise-constant approximation of such embeddings produces a graph-partition close to the objective segmentation. This loss is fully backpropagable, which allows us to learn vertex embeddings with deep learning algorithms. We evaluate our methods on a 3D point cloud oversegmentation task, defining a new state-of-the-art by a large margin. These results are based on the published work of Landrieu and Boussaha 2019. Comment: arXiv admin note: substantial text overlap with arXiv:1904.02113 |
| Databáze: | OpenAIRE |
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