We present a method for joint alignment of sparse in-the-wild image
collections of an object category. Most prior works assume either ground-truth
keypoint annotations or a large dataset of images of a single object category.
However, neither of the above assumptions hold true for the long-tail of the
objects present in the world. We present a self-supervised technique that
directly optimizes on a sparse collection of images of a particular
object/object category to obtain consistent dense correspondences across the
collection. We use pairwise nearest neighbors obtained from deep features of a
pre-trained vision transformer (ViT) model as noisy and sparse keypoint matches
and make them dense and accurate matches by optimizing a neural network that
jointly maps the image collection into a learned canonical grid. Experiments on
CUB and SPair-71k benchmarks demonstrate that our method can produce globally
consistent and higher quality correspondences across the image collection when
compared to existing self-supervised methods. Code and other material will be
made available at https://kampta.github.io/asic.

该研究提出了一种自监督技术的目标对象排列方法，使用神经网络将图像集映射到学习的规范网格，从而使得得到的密集一一对应点更加准确和稳定。

ASIC: 对野外稀疏图像集的对齐

ASIC: Aligning Sparse in-the-wild Image Collections

Representing the semantics of GUI screens and components is crucial to
data-driven computational methods for modeling user-GUI interactions and mining
GUI designs. Existing GUI semantic representations are limited to encoding
either the textual content, the visual design and layout patterns, or the app
contexts. Many representation techniques also require significant manual data
annotation efforts. This paper presents Screen2Vec, a new self-supervised
technique for generating representations in embedding vectors of GUI screens
and components that encode all of the above GUI features without requiring
manual annotation using the context of user interaction traces. Screen2Vec is
inspired by the word embedding method Word2Vec, but uses a new two-layer
pipeline informed by the structure of GUIs and interaction traces and
incorporates screen- and app-specific metadata. Through several sample
downstream tasks, we demonstrate Screen2Vec's key useful properties:
representing between-screen similarity through nearest neighbors,
composability, and capability to represent user tasks.

本文提出了 Screen2Vec 算法，该算法结合了 GUI 语义表示、嵌入向量和自监督的技术，在不需要手动注释的前提下，能够表示 GUI 屏幕和组件的所有特征，包括文字内容、视觉设计和布局模式、应用上下文，能够用于建模用户 - GUI 交互和挖掘 GUI 设计信息。

Screen2Vec: GUI 界面与组件的语义嵌入

Screen2Vec: Semantic Embedding of GUI Screens and GUI Components

Deep learning methods are successfully used in applications pertaining to
ubiquitous computing, health, and well-being. Specifically, the area of human
activity recognition (HAR) is primarily transformed by the convolutional and
recurrent neural networks, thanks to their ability to learn semantic
representations from raw input. However, to extract generalizable features,
massive amounts of well-curated data are required, which is a notoriously
challenging task; hindered by privacy issues, and annotation costs. Therefore,
unsupervised representation learning is of prime importance to leverage the
vast amount of unlabeled data produced by smart devices. In this work, we
propose a novel self-supervised technique for feature learning from sensory
data that does not require access to any form of semantic labels. We learn a
multi-task temporal convolutional network to recognize transformations applied
on an input signal. By exploiting these transformations, we demonstrate that
simple auxiliary tasks of the binary classification result in a strong
supervisory signal for extracting useful features for the downstream task. We
extensively evaluate the proposed approach on several publicly available
datasets for smartphone-based HAR in unsupervised, semi-supervised, and
transfer learning settings. Our method achieves performance levels superior to
or comparable with fully-supervised networks, and it performs significantly
better than autoencoders. Notably, for the semi-supervised case, the
self-supervised features substantially boost the detection rate by attaining a
kappa score between 0.7-0.8 with only 10 labeled examples per class. We get
similar impressive performance even if the features are transferred from a
different data source. While this paper focuses on HAR as the application
domain, the proposed technique is general and could be applied to a wide
variety of problems in other areas.

本文提出了一种基于自监督技术的特征学习方法，应用于无形标签的传感器数据。该方法可以应用于人类活动识别等领域，可以通过转化识别任务提供监督信号提取有价值的特征，并在智能手机环境下的无人监督、半监督和迁移学习的情况下实现了与完全监督网络相当甚至更好的性能。该技术可被广泛应用于其他领域。