Recent self-supervised contrastive learning methods greatly benefit from the
Siamese structure that aims at minimizing distances between positive pairs. For
high performance Siamese representation learning, one of the keys is to design
good contrastive pairs. Most previous works simply apply random sampling to
make different crops of the same image, which overlooks the semantic
information that may degrade the quality of views. In this work, we propose
ContrastiveCrop, which could effectively generate better crops for Siamese
representation learning. Firstly, a semantic-aware object localization strategy
is proposed within the training process in a fully unsupervised manner. This
guides us to generate contrastive views which could avoid most false positives
(i.e., object vs. background). Moreover, we empirically find that views with
similar appearances are trivial for the Siamese model training. Thus, a
center-suppressed sampling is further designed to enlarge the variance of
crops. Remarkably, our method takes a careful consideration of positive pairs
for contrastive learning with negligible extra training overhead. As a
plug-and-play and framework-agnostic module, ContrastiveCrop consistently
improves SimCLR, MoCo, BYOL, SimSiam by 0.4% ~ 2.0% classification accuracy on
CIFAR-10, CIFAR-100, Tiny ImageNet and STL-10. Superior results are also
achieved on downstream detection and segmentation tasks when pre-trained on
ImageNet-1K.

提出了 ContrastiveCrop，一种用于 Siamese 表示学习的更好对比对的生成方法，该方法采用全无监督方式提出了基于语义的目标定位策略并进一步设计了中心抑制采样来扩大裁剪区域的方差，成功提高了自我监督学习中 SimCLR，MoCo，BYOL，SimSiam 等方法的分类准确率，同时在基于 ImageNet-1K 预训练的下游检测和分割任务上取得了优异的结果。

构建更好的对比视图，用于孪生神经网络表示学习

Crafting Better Contrastive Views for Siamese Representation Learning

Despite having promising results, style transfer, which requires preparing
style images in advance, may result in lack of creativity and accessibility.
Following human instruction, on the other hand, is the most natural way to
perform artistic style transfer that can significantly improve controllability
for visual effect applications. We introduce a new task, language-driven
artistic style transfer (LDAST), to manipulate the style of a content image,
guided by a text. We propose contrastive language visual artist (CLVA) that
learns to extract visual semantics from style instructions and accomplish LDAST
by the patch-wise style discriminator. The discriminator considers the
correlation between language and patches of style images or transferred results
to jointly embed style instructions. CLVA further compares contrastive pairs of
content images and style instructions to improve the mutual relativeness. The
results from the same content image can preserve consistent content structures.
Besides, they should present analogous style patterns from style instructions
that contain similar visual semantics. The experiments show that our CLVA is
effective and achieves superb transferred results on LDAST.

引入了一个新的任务，即以语言为驱动的艺术风格转移（LDAST），通过文本引导操纵内容图像的风格，所提出的对比语言视觉艺术家（CLVA）可以从风格指令中提取视觉语义并通过分块风格鉴别器实现 LDAST。