Transposed convolution is crucial for generating high-resolution outputs, yet
has received little attention compared to convolution layers. In this work we
revisit transposed convolution and introduce a novel layer that allows us to
place information in the image selectively and choose the `stroke breadth' at
which the image is synthesized, whilst incurring a small additional parameter
cost. For this we introduce three ideas: firstly, we regress offsets to the
positions where the transpose convolution results are placed; secondly we
broadcast the offset weight locations over a learnable neighborhood; and
thirdly we use a compact parametrization to share weights and restrict offsets.
We show that simply substituting upsampling operators with our novel layer
produces substantial improvements across tasks as diverse as instance
segmentation, object detection, semantic segmentation, generative image
modeling, and 3D magnetic resonance image enhancement, while outperforming all
existing variants of transposed convolutions. Our novel layer can be used as a
drop-in replacement for 2D and 3D upsampling operators and the code will be
publicly available.

本文介绍了一种新的层用于选择性地在图像中放置信息，并选择图像合成的笔划宽度。方法利用了三个想法：首先，回归推断出转置卷积结果的位置偏移量；其次，将偏移位置权重广播到可学习的邻域；第三，利用紧凑参数化共享权重和限制偏移量。最终结果表明，该方法可应用于诸如实例分割、目标检测、语义分割、生成图像建模和 3D 磁共振图像增强等多种任务，并优于现有的转置卷积变体。

可变形尺度的转置卷积

Deformably-Scaled Transposed Convolution

We show that existing upsampling operators can be unified with the notion of
the index function. This notion is inspired by an observation in the decoding
process of deep image matting where indices-guided unpooling can recover
boundary details much better than other upsampling operators such as bilinear
interpolation. By looking at the indices as a function of the feature map, we
introduce the concept of learning to index, and present a novel index-guided
encoder-decoder framework where indices are self-learned adaptively from data
and are used to guide the pooling and upsampling operators, without the need of
supervision. At the core of this framework is a flexible network module, termed
IndexNet, which dynamically predicts indices given an input. Due to its
flexibility, IndexNet can be used as a plug-in applying to any off-the-shelf
convolutional networks that have coupled downsampling and upsampling stages.
We demonstrate the effectiveness of IndexNet on the task of natural image
matting where the quality of learned indices can be visually observed from
predicted alpha mattes. Results on the Composition-1k matting dataset show that
our model built on MobileNetv2 exhibits at least $16.1\%$ improvement over the
seminal VGG-16 based deep matting baseline, with less training data and lower
model capacity. Code and models has been made available at:
this https URL

该研究提出了一种基于自学习的指标引导的编码器 - 解码器框架，通过自适应地从数据中学习指标来指导池化和上采样操作，实现了对现有的上采样运算符进行统一的方法，并在自然图像抠图任务中取得了良好的效果。