We introduce a practical Domain Adaptation (DA) paradigm called
Class-Incremental Domain Adaptation (CIDA). Existing DA methods tackle
domain-shift but are unsuitable for learning novel target-domain classes.
Meanwhile, class-incremental (CI) methods enable learning of new classes in
absence of source training data but fail under a domain-shift without labeled
supervision. In this work, we effectively identify the limitations of these
approaches in the CIDA paradigm. Motivated by theoretical and empirical
observations, we propose an effective method, inspired by prototypical
networks, that enables classification of target samples into both shared and
novel (one-shot) target classes, even under a domain-shift. Our approach yields
superior performance as compared to both DA and CI methods in the CIDA
paradigm.

该研究提出了一种实用的域自适应范式 —— 增量类别域自适应（Class-Incremental Domain Adaptation，CIDA），并且根据理论和实证观察，提出了一种基于样板网络的有效方法，使得在域偏移的情况下可以对目标样本进行分类，并且可以对共享的和新的目标类进行分类，其性能优于 CIDA 范式中的 DA 和 CI 方法。

类增量领域自适应

Class-Incremental Domain Adaptation

Zero-shot learning (ZSL) is a challenging problem that aims to recognize the
target categories without seen data, where semantic information is leveraged to
transfer knowledge from some source classes. Although ZSL has made great
progress in recent years, most existing approaches are easy to overfit the
sources classes in generalized zero-shot learning (GZSL) task, which indicates
that they learn little knowledge about target classes. To tackle such problem,
we propose a novel Transferable Contrastive Network (TCN) that explicitly
transfers knowledge from the source classes to the target classes. It
automatically contrasts one image with different classes to judge whether they
are consistent or not. By exploiting the class similarities to make knowledge
transfer from source images to similar target classes, our approach is more
robust to recognize the target images. Experiments on five benchmark datasets
show the superiority of our approach for GZSL.

本文提出了一种新的 Transferable Contrastive Network (TCN) 方法，旨在从源图像中将知识传输到目标类中，以提高 GZSL 识别的鲁棒性。实验证明，该方法优于现有方法。

可传递的对比网络用于广义零样本学习

Transferable Contrastive Network for Generalized Zero-Shot Learning

Many automated processes such as auto-piloting rely on a good semantic
segmentation as a critical component. To speed up performance, it is common to
downsample the input frame. However, this comes at the cost of missed small
objects and reduced accuracy at semantic boundaries. To address this problem,
we propose a new content-adaptive downsampling technique that learns to favor
sampling locations near semantic boundaries of target classes. Cost-performance
analysis shows that our method consistently outperforms the uniform sampling
improving balance between accuracy and computational efficiency. Our adaptive
sampling gives segmentation with better quality of boundaries and more reliable
support for smaller-size objects.

本研究提出了一种新的内容自适应下采样技术，通过学习目标类别边界附近的采样位置，保证了准确性和计算效率之间的平衡，从而获得更好的边界质量和对小物体的更可靠支持。