7,736 research outputs found
Deep Discrete Hashing with Self-supervised Pairwise Labels
Hashing methods have been widely used for applications of large-scale image
retrieval and classification. Non-deep hashing methods using handcrafted
features have been significantly outperformed by deep hashing methods due to
their better feature representation and end-to-end learning framework. However,
the most striking successes in deep hashing have mostly involved discriminative
models, which require labels. In this paper, we propose a novel unsupervised
deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image
retrieval and classification. In the proposed framework, we address two main
problems: 1) how to directly learn discrete binary codes? 2) how to equip the
binary representation with the ability of accurate image retrieval and
classification in an unsupervised way? We resolve these problems by introducing
an intermediate variable and a loss function steering the learning process,
which is based on the neighborhood structure in the original space.
Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17)
demonstrate that our DDH significantly outperforms existing hashing methods by
large margin in terms of~mAP for image retrieval and object recognition. Code
is available at \url{https://github.com/htconquer/ddh}
Parametric t-Distributed Stochastic Exemplar-centered Embedding
Parametric embedding methods such as parametric t-SNE (pt-SNE) have been
widely adopted for data visualization and out-of-sample data embedding without
further computationally expensive optimization or approximation. However, the
performance of pt-SNE is highly sensitive to the hyper-parameter batch size due
to conflicting optimization goals, and often produces dramatically different
embeddings with different choices of user-defined perplexities. To effectively
solve these issues, we present parametric t-distributed stochastic
exemplar-centered embedding methods. Our strategy learns embedding parameters
by comparing given data only with precomputed exemplars, resulting in a cost
function with linear computational and memory complexity, which is further
reduced by noise contrastive samples. Moreover, we propose a shallow embedding
network with high-order feature interactions for data visualization, which is
much easier to tune but produces comparable performance in contrast to a deep
neural network employed by pt-SNE. We empirically demonstrate, using several
benchmark datasets, that our proposed methods significantly outperform pt-SNE
in terms of robustness, visual effects, and quantitative evaluations.Comment: fixed typo
Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval
In this paper, we propose a novel deep generative approach to cross-modal
retrieval to learn hash functions in the absence of paired training samples
through the cycle consistency loss. Our proposed approach employs adversarial
training scheme to lean a couple of hash functions enabling translation between
modalities while assuming the underlying semantic relationship. To induce the
hash codes with semantics to the input-output pair, cycle consistency loss is
further proposed upon the adversarial training to strengthen the correlations
between inputs and corresponding outputs. Our approach is generative to learn
hash functions such that the learned hash codes can maximally correlate each
input-output correspondence, meanwhile can also regenerate the inputs so as to
minimize the information loss. The learning to hash embedding is thus performed
to jointly optimize the parameters of the hash functions across modalities as
well as the associated generative models. Extensive experiments on a variety of
large-scale cross-modal data sets demonstrate that our proposed method achieves
better retrieval results than the state-of-the-arts.Comment: To appeared on IEEE Trans. Image Processing. arXiv admin note: text
overlap with arXiv:1703.10593 by other author
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