End-to-End Trainable Triangulation Embedding
This code implements end-to-end trainable Triangulation Embedding layer. The work is inspired by NetVLAD, an end-to-end trainable VLAD layer.
The module was implemented & tested in TensorFlow 1.8.0. NetTriangulationEmbedding is distributed under Apache-2 License (see the
NetTriangulationEmbedding has potential usages in image classification and retrieval tasks. It is applicable to end-to-end trainable models, which is an improvement from origianl Triangulation Embedding method described in .
from NetTriangulationEmbedding import TriangulationEmbeddingModule, TemporalDifferenceDescriptors import tensorflow as tf te = TriangulationEmbedding(feature_size=1024, num_descriptors=30, num_anchor=16, add_batch_norm=True, is_training=True) with tf.variable_scope("t_emb"): activation = te.forward(tensor)
Please note that we are not the author of the following reference.
 Jégou, Hervé, and Andrew Zisserman. "Triangulation embedding and democratic aggregation for image search." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
- 1.00 (08 July 2018)
- Initial public release
- 1.01 (17 September 2018)
- Removed class TemporalDifferenceDescriptors. The purpose of this class was to calculate the optical flow in a high dimensional vector.