15,516 research outputs found
Multimodal Co-learning: A Domain Adaptation Method for Building Extraction from Optical Remote Sensing Imagery
In this paper, we aim to improve the transfer learning ability of 2D convolutional neural networks (CNNs) for building extraction from optical imagery and digital surface models (DSMs) using a 2D-3D co-learning framework. Unlabeled target domain data are incorporated as unlabeled training data pairs to optimize the training procedure. Our framework adaptively transfers unsupervised mutual information between the 2D and 3D modality (i.e., DSM-derived point clouds) during the training phase via a soft connection, utilizing a predefined loss function. Experimental results from a spaceborne-to-airborne cross-domain case demonstrate that the framework we present can quantitatively and qualitatively improve the testing results for building extraction from single-modality optical images
Self-Supervised Intrinsic Image Decomposition
Intrinsic decomposition from a single image is a highly challenging task, due
to its inherent ambiguity and the scarcity of training data. In contrast to
traditional fully supervised learning approaches, in this paper we propose
learning intrinsic image decomposition by explaining the input image. Our
model, the Rendered Intrinsics Network (RIN), joins together an image
decomposition pipeline, which predicts reflectance, shape, and lighting
conditions given a single image, with a recombination function, a learned
shading model used to recompose the original input based off of intrinsic image
predictions. Our network can then use unsupervised reconstruction error as an
additional signal to improve its intermediate representations. This allows
large-scale unlabeled data to be useful during training, and also enables
transferring learned knowledge to images of unseen object categories, lighting
conditions, and shapes. Extensive experiments demonstrate that our method
performs well on both intrinsic image decomposition and knowledge transfer.Comment: NIPS 2017 camera-ready version, project page:
http://rin.csail.mit.edu
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