Name
Title | ||
|---|---|---|
Learning Spatial Pyramid Attentive Pooling in Image Synthesis and Image-to-Image Translation. |
Abstract | ||
|---|---|---|
Image synthesis and image-to-image translation are two important generative learning tasks. Remarkable progress has been made by learning Generative Adversarial Networks (GANs)~cite{goodfellow2014generative} and cycle-consistent GANs (CycleGANs)~cite{zhu2017unpaired} respectively. This paper presents a method of learning Spatial Pyramid Attentive Pooling (SPAP) which is a novel architectural unit and can be easily integrated into both generators and discriminators in GANs and CycleGANs. The proposed SPAP integrates Atrous spatial pyramid~cite{chen2018deeplab}, a proposed cascade attention mechanism and residual connections~cite{he2016deep}. It leverages the advantages of the three components to facilitate effective end-to-end generative learning: (i) the capability of fusing multi-scale information by ASPP; (ii) the capability of capturing relative importance between both spatial locations (especially multi-scale context) or feature channels by attention; (iii) the capability of preserving information and enhancing optimization feasibility by residual connections. Coarse-to-fine and fine-to-coarse SPAP are studied and intriguing attention maps are observed in both tasks. In experiments, the proposed SPAP is tested in GANs on the Celeba-HQ-128 dataset~cite{karras2017progressive}, and tested in CycleGANs on the Image-to-Image translation datasets including the Cityscape dataset~cite{cordts2016cityscapes}, Facade and Aerial Maps dataset~cite{zhu2017unpaired}, both obtaining better performance. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Computer Vision and Pattern Recognition | Journal |
Volume | Citations | PageRank |
abs/1901.06322 | 0 | 0.34 |
References | Authors | |
28 | 2 | |