Segment Anything Model (SAM) is a foundation model for interactive
segmentation, and it has catalyzed major advances in generative AI,
computational photography, and medical imaging. This model takes in an
arbitrary user input and provides segmentation masks of the corresponding
objects. It is our goal to develop a version of SAM that is appropriate for use
in a photography app. The original SAM model has a few challenges in this
setting. First, original SAM a 600 million parameter based on ViT-H, and its
high computational cost and large model size that are not suitable for todays
mobile hardware. We address this by proposing the SqueezeSAM model
architecture, which is 50x faster and 100x smaller than SAM. Next, when a user
takes a photo on their phone, it might not occur to them to click on the image
and get a mask. Our solution is to use salient object detection to generate the
first few clicks. This produces an initial segmentation mask that the user can
interactively edit. Finally, when a user clicks on an object, they typically
expect all related pieces of the object to be segmented. For instance, if a
user clicks on a person t-shirt in a photo, they expect the whole person to be
segmented, but SAM typically segments just the t-shirt. We address this with a
new data augmentation scheme, and the end result is that if the user clicks on
a person holding a basketball, the person and the basketball are all segmented
together.

我们旨在开发一个适用于摄影应用的 SAM 模型的版本。SqueezeSAM 模型体系结构在性能和模型尺寸上相比原模型具有更高的效果，我们使用显著性物体检测生成初始分割掩码，用户可以进行交互编辑，并通过引入新的数据增强方案来实现期望点击相关物体的整体分割。

SqueezeSAM: 用户友好的移动交互分割

SqueezeSAM: User friendly mobile interactive segmentation

The lack of labeled data is a key challenge for learning useful
representation from time series data. However, an unsupervised representation
framework that is capable of producing high quality representations could be of
great value. It is key to enabling transfer learning, which is especially
beneficial for medical applications, where there is an abundance of data but
labeling is costly and time consuming. We propose an unsupervised contrastive
learning framework that is motivated from the perspective of label smoothing.
The proposed approach uses a novel contrastive loss that naturally exploits a
data augmentation scheme in which new samples are generated by mixing two data
samples with a mixing component. The task in the proposed framework is to
predict the mixing component, which is utilized as soft targets in the loss
function. Experiments demonstrate the framework's superior performance compared
to other representation learning approaches on both univariate and multivariate
time series and illustrate its benefits for transfer learning for clinical time
series.

提出了一种新的基于对比损失的无监督表示学习框架，并通过利用数据增广方案生成新样本来优化损失函数，实现混合样本的预测任务，以利用软目标进行学习。实验结果表明本方法在单变量和多变量时间序列上的表现优于其他表示学习方法，并展示了它对于临床时间序列的传递学习的好处。