1,238 research outputs found
High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks
Synthesizing face sketches from real photos and its inverse have many
applications. However, photo/sketch synthesis remains a challenging problem due
to the fact that photo and sketch have different characteristics. In this work,
we consider this task as an image-to-image translation problem and explore the
recently popular generative models (GANs) to generate high-quality realistic
photos from sketches and sketches from photos. Recent GAN-based methods have
shown promising results on image-to-image translation problems and
photo-to-sketch synthesis in particular, however, they are known to have
limited abilities in generating high-resolution realistic images. To this end,
we propose a novel synthesis framework called Photo-Sketch Synthesis using
Multi-Adversarial Networks, (PS2-MAN) that iteratively generates low resolution
to high resolution images in an adversarial way. The hidden layers of the
generator are supervised to first generate lower resolution images followed by
implicit refinement in the network to generate higher resolution images.
Furthermore, since photo-sketch synthesis is a coupled/paired translation
problem, we leverage the pair information using CycleGAN framework. Both Image
Quality Assessment (IQA) and Photo-Sketch Matching experiments are conducted to
demonstrate the superior performance of our framework in comparison to existing
state-of-the-art solutions. Code available at:
https://github.com/lidan1/PhotoSketchMAN.Comment: Accepted by 2018 13th IEEE International Conference on Automatic Face
& Gesture Recognition (FG 2018)(Oral
Rethinking the Domain Gap in Near-infrared Face Recognition
Heterogeneous face recognition (HFR) involves the intricate task of matching
face images across the visual domains of visible (VIS) and near-infrared (NIR).
While much of the existing literature on HFR identifies the domain gap as a
primary challenge and directs efforts towards bridging it at either the input
or feature level, our work deviates from this trend. We observe that large
neural networks, unlike their smaller counterparts, when pre-trained on large
scale homogeneous VIS data, demonstrate exceptional zero-shot performance in
HFR, suggesting that the domain gap might be less pronounced than previously
believed. By approaching the HFR problem as one of low-data fine-tuning, we
introduce a straightforward framework: comprehensive pre-training, succeeded by
a regularized fine-tuning strategy, that matches or surpasses the current
state-of-the-art on four publicly available benchmarks. Corresponding codes can
be found at https://github.com/michaeltrs/RethinkNIRVIS.Comment: 5 pages, 3 figures, 6 table
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