2,316 research outputs found
From Facial Parts Responses to Face Detection: A Deep Learning Approach
In this paper, we propose a novel deep convolutional network (DCN) that
achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically,
our method achieves a high recall rate of 90.99% on the challenging FDDB
benchmark, outperforming the state-of-the-art method by a large margin of
2.91%. Importantly, we consider finding faces from a new perspective through
scoring facial parts responses by their spatial structure and arrangement. The
scoring mechanism is carefully formulated considering challenging cases where
faces are only partially visible. This consideration allows our network to
detect faces under severe occlusion and unconstrained pose variation, which are
the main difficulty and bottleneck of most existing face detection approaches.
We show that despite the use of DCN, our network can achieve practical runtime
speed.Comment: To appear in ICCV 201
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
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