25,872 research outputs found
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution
In many computer vision applications, obtaining images of high resolution in
both the spatial and spectral domains are equally important. However, due to
hardware limitations, one can only expect to acquire images of high resolution
in either the spatial or spectral domains. This paper focuses on hyperspectral
image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low
spatial resolution (LR) but high spectral resolution is fused with a
multispectral image (MSI) with high spatial resolution (HR) but low spectral
resolution to obtain HR HSI. Existing deep learning-based solutions are all
supervised that would need a large training set and the availability of HR HSI,
which is unrealistic. Here, we make the first attempt to solving the HSI-SR
problem using an unsupervised encoder-decoder architecture that carries the
following uniquenesses. First, it is composed of two encoder-decoder networks,
coupled through a shared decoder, in order to preserve the rich spectral
information from the HSI network. Second, the network encourages the
representations from both modalities to follow a sparse Dirichlet distribution
which naturally incorporates the two physical constraints of HSI and MSI.
Third, the angular difference between representations are minimized in order to
reduce the spectral distortion. We refer to the proposed architecture as
unsupervised Sparse Dirichlet-Net, or uSDN. Extensive experimental results
demonstrate the superior performance of uSDN as compared to the
state-of-the-art.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2018, Spotlight
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification
Existing person re-identification (re-id) methods rely mostly on either
localised or global feature representation alone. This ignores their joint
benefit and mutual complementary effects. In this work, we show the advantages
of jointly learning local and global features in a Convolutional Neural Network
(CNN) by aiming to discover correlated local and global features in different
context. Specifically, we formulate a method for joint learning of local and
global feature selection losses designed to optimise person re-id when using
only generic matching metrics such as the L2 distance. We design a novel CNN
architecture for Jointly Learning Multi-Loss (JLML) of local and global
discriminative feature optimisation subject concurrently to the same re-id
labelled information. Extensive comparative evaluations demonstrate the
advantages of this new JLML model for person re-id over a wide range of
state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03,
Market-1501).Comment: Accepted by IJCAI 201
SPINE: SParse Interpretable Neural Embeddings
Prediction without justification has limited utility. Much of the success of
neural models can be attributed to their ability to learn rich, dense and
expressive representations. While these representations capture the underlying
complexity and latent trends in the data, they are far from being
interpretable. We propose a novel variant of denoising k-sparse autoencoders
that generates highly efficient and interpretable distributed word
representations (word embeddings), beginning with existing word representations
from state-of-the-art methods like GloVe and word2vec. Through large scale
human evaluation, we report that our resulting word embedddings are much more
interpretable than the original GloVe and word2vec embeddings. Moreover, our
embeddings outperform existing popular word embeddings on a diverse suite of
benchmark downstream tasks.Comment: AAAI 201
- …