4,164 research outputs found
Hierarchy Composition GAN for High-fidelity Image Synthesis
Despite the rapid progress of generative adversarial networks (GANs) in image
synthesis in recent years, the existing image synthesis approaches work in
either geometry domain or appearance domain alone which often introduces
various synthesis artifacts. This paper presents an innovative Hierarchical
Composition GAN (HIC-GAN) that incorporates image synthesis in geometry and
appearance domains into an end-to-end trainable network and achieves superior
synthesis realism in both domains simultaneously. We design an innovative
hierarchical composition mechanism that is capable of learning realistic
composition geometry and handling occlusions while multiple foreground objects
are involved in image composition. In addition, we introduce a novel attention
mask mechanism that guides to adapt the appearance of foreground objects which
also helps to provide better training reference for learning in geometry
domain. Extensive experiments on scene text image synthesis, portrait editing
and indoor rendering tasks show that the proposed HIC-GAN achieves superior
synthesis performance qualitatively and quantitatively.Comment: 11 pages, 8 figure
Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis
Polarimetric thermal to visible face verification entails matching two images
that contain significant domain differences. Several recent approaches have
attempted to synthesize visible faces from thermal images for cross-modal
matching. In this paper, we take a different approach in which rather than
focusing only on synthesizing visible faces from thermal faces, we also propose
to synthesize thermal faces from visible faces. Our intuition is based on the
fact that thermal images also contain some discriminative information about the
person for verification. Deep features from a pre-trained Convolutional Neural
Network (CNN) are extracted from the original as well as the synthesized
images. These features are then fused to generate a template which is then used
for verification. The proposed synthesis network is based on the self-attention
generative adversarial network (SAGAN) which essentially allows efficient
attention-guided image synthesis. Extensive experiments on the ARL polarimetric
thermal face dataset demonstrate that the proposed method achieves
state-of-the-art performance.Comment: This work is accepted at the 12th IAPR International Conference On
Biometrics (ICB 2019
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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