5,410 research outputs found
ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
We propose a structured prediction architecture, which exploits the local
generic features extracted by Convolutional Neural Networks and the capacity of
Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed
architecture, called ReSeg, is based on the recently introduced ReNet model for
image classification. We modify and extend it to perform the more challenging
task of semantic segmentation. Each ReNet layer is composed of four RNN that
sweep the image horizontally and vertically in both directions, encoding
patches or activations, and providing relevant global information. Moreover,
ReNet layers are stacked on top of pre-trained convolutional layers, benefiting
from generic local features. Upsampling layers follow ReNet layers to recover
the original image resolution in the final predictions. The proposed ReSeg
architecture is efficient, flexible and suitable for a variety of semantic
segmentation tasks. We evaluate ReSeg on several widely-used semantic
segmentation datasets: Weizmann Horse, Oxford Flower, and CamVid; achieving
state-of-the-art performance. Results show that ReSeg can act as a suitable
architecture for semantic segmentation tasks, and may have further applications
in other structured prediction problems. The source code and model
hyperparameters are available on https://github.com/fvisin/reseg.Comment: In CVPR Deep Vision Workshop, 201
Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks
We propose a method for embedding two-dimensional locations in a continuous
vector space using a neural network-based model incorporating mixtures of
Gaussian distributions, presenting two model variants for text-based
geolocation and lexical dialectology. Evaluated over Twitter data, the proposed
model outperforms conventional regression-based geolocation and provides a
better estimate of uncertainty. We also show the effectiveness of the
representation for predicting words from location in lexical dialectology, and
evaluate it using the DARE dataset.Comment: Conference on Empirical Methods in Natural Language Processing (EMNLP
2017) September 2017, Copenhagen, Denmar
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