From b47a3f46360a640e03a052074ecdc3922d216577 Mon Sep 17 00:00:00 2001 From: devisperessutti Date: Mon, 13 Jul 2020 10:49:55 +0200 Subject: [PATCH] update README with PSE-TAE description --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index d68a80f..786102f 100644 --- a/README.md +++ b/README.md @@ -66,5 +66,8 @@ Classification models for crop classification using time-series: * **TempCNN**: Implementation of the TempCNN network taken from the [temporalCNN implementation of Charlotte Pelletier](https://github.com/charlotte-pel/temporalCNN). * **Recurrent NN**: Implementation of (bidirectional) Recurrent Neural Networks for the classification of time-series. Implementation allows to use either `SimpleRNN`, `GRU` or `LSTM` layers as building blocks of the architecture. * **TransformerEncoder**: Implementation of a time-series classification architecture based on [self-attention](https://arxiv.org/abs/1706.03762) layers. This implementation follows [this PyTorch implementation of Marc Russwurm](https://github.com/MarcCoru/crop-type-mapping). +* **PSE+TAE**: Implementation of the Pixel-Set Encoder and temporal Self-attention proposed in Garnot V. _et al._ +["Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention"](https://hal.archives-ouvertes.fr/hal-02879223/document). +This implementation is adapted from the [Pytorch version](https://github.com/VSainteuf/pytorch-psetae). Descriptions and examples of semantic segmentation architectures are available [here](MODELS.md).