Publication

LIFT: Learned Invariant Feature Transform

Pascal Fua, Vincent Lepetit, Kwang Moo Yi, Eduard Trulls Fortuny
2016
Conference Papers

Abstract

We introduce a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description. While previous works have successfully tackled each one of these problems individually, we show how to learn to do all three in a unified manner while preserving end-to-end differentiability. We then demonstrate that our Deep pipeline outperforms state-of-the-art methods on a number of benchmark datasets, without the need of retraining.

Official source

https://infoscience.epfl.ch/entities/publication/0a5dc45d-65bb-49df-835b-6f62f1f5c30a

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Pascal Fua, Vincent Lepetit, Kwang Moo Yi, Eduard Trulls Fortuny
2016
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/0a5dc45d-65bb-49df-835b-6f62f1f5c30a

About this result

Related concepts (9)

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

Deep belief network

In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. When trained on a set of examples without supervision, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as feature detectors. After this learning step, a DBN can be further trained with supervision to perform classification.

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LIFT: Learned Invariant Feature Transform

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