Deep Shape Matching
arXiv:1709.03409
Abstract
We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient methods are chosen to turn the images into edge maps. Secondly, the network is trained with edge maps of landmark images, which are automatically obtained by a structure-from-motion pipeline. The learned representation is evaluated on a range of different tasks, providing improvements on challenging cases of domain generalization, generic sketch-based image retrieval or its fine-grained counterpart. In contrast to other methods that learn a different model per task, object category, or domain, we use the same network throughout all our experiments, achieving state-of-the-art results in multiple benchmarks.
ECCV 2018
References in corpus (6)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Domain Separation Networks
- Domain Generalization via Invariant Feature Representation
- Sketch-based 3D Shape Retrieval using Convolutional Neural Networks
- Generalisation and Sharing in Triplet Convnets for Sketch based Visual Search
- Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval