Foreground-aware image synthesis aims to generate images as well as their
foreground masks. A common approach is to formulate an image as an masked
blending of a foreground image and a background image. It is a challenging
problem because it is prone to reach the trivial solution where either image
overwhelms the other, i.e., the masks become completely full or empty, and the
foreground and background are not meaningfully separated. We present FurryGAN
with three key components: 1) imposing both the foreground image and the
composite image to be realistic, 2) designing a mask as a combination of coarse
and fine masks, and 3) guiding the generator by an auxiliary mask predictor in
the discriminator. Our method produces realistic images with remarkably
detailed alpha masks which cover hair, fur, and whiskers in a fully
unsupervised manner.

FurryGAN 是一种前景感知图像合成方法，通过引入具有粗细融合的蒙版、辅助蒙版预测器以及使得前景图像和合成图像都具有逼真性等关键组件，能够在无需人工监督的情况下生成具有毛发、皮毛和胡须的逼真图像和详细的透明度蒙版。

FurryGAN：高质量前景感知图像合成

FurryGAN: High Quality Foreground-aware Image Synthesis

Referring image segmentation is a fundamental vision-language task that aims
to segment out an object referred to by a natural language expression from an
image. One of the key challenges behind this task is leveraging the referring
expression for highlighting relevant positions in the image. A paradigm for
tackling this problem is to leverage a powerful vision-language ("cross-modal")
decoder to fuse features independently extracted from a vision encoder and a
language encoder. Recent methods have made remarkable advancements in this
paradigm by exploiting Transformers as cross-modal decoders, concurrent to the
Transformer's overwhelming success in many other vision-language tasks.
Adopting a different approach in this work, we show that significantly better
cross-modal alignments can be achieved through the early fusion of linguistic
and visual features in intermediate layers of a vision Transformer encoder
network. By conducting cross-modal feature fusion in the visual feature
encoding stage, we can leverage the well-proven correlation modeling power of a
Transformer encoder for excavating helpful multi-modal context. This way,
accurate segmentation results are readily harvested with a light-weight mask
predictor. Without bells and whistles, our method surpasses the previous
state-of-the-art methods on RefCOCO, RefCOCO+, and G-Ref by large margins.

本研究提出一种新的方法，在视觉 Transformer 编码器网络的中间层通过对语言和视觉特征进行交叉融合，实现更好的交叉模态对齐，进而通过轻量级的掩模预测器得到准确的分割结果，该方法在 RefCOCO、RefCOCO + 和 G-Ref 数据集上均超越了以往的最优方法。