623 research outputs found
Unsupervised Generative Adversarial Cross-modal Hashing
Cross-modal hashing aims to map heterogeneous multimedia data into a common
Hamming space, which can realize fast and flexible retrieval across different
modalities. Unsupervised cross-modal hashing is more flexible and applicable
than supervised methods, since no intensive labeling work is involved. However,
existing unsupervised methods learn hashing functions by preserving inter and
intra correlations, while ignoring the underlying manifold structure across
different modalities, which is extremely helpful to capture meaningful nearest
neighbors of different modalities for cross-modal retrieval. To address the
above problem, in this paper we propose an Unsupervised Generative Adversarial
Cross-modal Hashing approach (UGACH), which makes full use of GAN's ability for
unsupervised representation learning to exploit the underlying manifold
structure of cross-modal data. The main contributions can be summarized as
follows: (1) We propose a generative adversarial network to model cross-modal
hashing in an unsupervised fashion. In the proposed UGACH, given a data of one
modality, the generative model tries to fit the distribution over the manifold
structure, and select informative data of another modality to challenge the
discriminative model. The discriminative model learns to distinguish the
generated data and the true positive data sampled from correlation graph to
achieve better retrieval accuracy. These two models are trained in an
adversarial way to improve each other and promote hashing function learning.
(2) We propose a correlation graph based approach to capture the underlying
manifold structure across different modalities, so that data of different
modalities but within the same manifold can have smaller Hamming distance and
promote retrieval accuracy. Extensive experiments compared with 6
state-of-the-art methods verify the effectiveness of our proposed approach.Comment: 8 pages, accepted by 32th AAAI Conference on Artificial Intelligence
(AAAI), 201
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
SSAH: Semi-supervised Adversarial Deep Hashing with Self-paced Hard Sample Generation
Deep hashing methods have been proved to be effective and efficient for
large-scale Web media search. The success of these data-driven methods largely
depends on collecting sufficient labeled data, which is usually a crucial
limitation in practical cases. The current solutions to this issue utilize
Generative Adversarial Network (GAN) to augment data in semi-supervised
learning. However, existing GAN-based methods treat image generations and
hashing learning as two isolated processes, leading to generation
ineffectiveness. Besides, most works fail to exploit the semantic information
in unlabeled data. In this paper, we propose a novel Semi-supervised Self-pace
Adversarial Hashing method, named SSAH to solve the above problems in a unified
framework. The SSAH method consists of an adversarial network (A-Net) and a
hashing network (H-Net). To improve the quality of generative images, first,
the A-Net learns hard samples with multi-scale occlusions and multi-angle
rotated deformations which compete against the learning of accurate hashing
codes. Second, we design a novel self-paced hard generation policy to gradually
increase the hashing difficulty of generated samples. To make use of the
semantic information in unlabeled ones, we propose a semi-supervised consistent
loss. The experimental results show that our method can significantly improve
state-of-the-art models on both the widely-used hashing datasets and
fine-grained datasets
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