Most of the research in content-based image retrieval (CBIR) focus on
developing robust feature representations that can effectively retrieve
instances from a database of images that are visually similar to a query.
However, the retrieved images sometimes contain results that are not
semantically related to the query. To address this, we propose a method for
CBIR that captures both visual and semantic similarity using a visual
hierarchy. The hierarchy is constructed by merging classes with overlapping
features in the latent space of a deep neural network trained for
classification, assuming that overlapping classes share high visual and
semantic similarities. Finally, the constructed hierarchy is integrated into
the distance calculation metric for similarity search. Experiments on standard
datasets: CUB-200-2011 and CIFAR100, and a real-life use case using diatom
microscopy images show that our method achieves superior performance compared
to the existing methods on image retrieval.

提出了一种基于内容的图像检索方法，通过构建视觉层次结构来捕捉视觉和语义的相似性，将其融合到相似度搜索的距离计算度量中，实验证明该方法在图像检索方面具有优越性能。

基于层次结构的视觉和语义相似度融合图像检索

Integrating Visual and Semantic Similarity Using Hierarchies for Image  Retrieval

Recently, it has been widely known that deep neural networks are highly
vulnerable and easily broken by adversarial attacks. To mitigate the
adversarial vulnerability, many defense algorithms have been proposed.
Recently, to improve adversarial robustness, many works try to enhance feature
representation by imposing more direct supervision on the discriminative
feature. However, existing approaches lack an understanding of learning
adversarially robust feature representation. In this paper, we propose a novel
training framework called Robust Proxy Learning. In the proposed method, the
model explicitly learns robust feature representations with robust proxies. To
this end, firstly, we demonstrate that we can generate class-representative
robust features by adding class-wise robust perturbations. Then, we use the
class representative features as robust proxies. With the class-wise robust
features, the model explicitly learns adversarially robust features through the
proposed robust proxy learning framework. Through extensive experiments, we
verify that we can manually generate robust features, and our proposed learning
framework could increase the robustness of the DNNs.

本文提出了一种名为 Robust Proxy Learning 的训练框架，该框架可以通过生成代表类的健壮特征并将其用作健壮代理来明确地学习对抗性健壮特征表征，进而提高深度神经网络的健壮性。

稳健代理：通过稳健代理学习提高对抗性稳健性

Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning

Few-shot learning aims to learn a new concept when only a few training
examples are available, which has been extensively explored in recent years.
However, most of the current works heavily rely on a large-scale labeled
auxiliary set to train their models in an episodic-training paradigm. Such a
kind of supervised setting basically limits the widespread use of few-shot
learning algorithms. Instead, in this paper, we develop a novel framework
called Unsupervised Few-shot Learning via Distribution Shift-based Data
Augmentation (ULDA), which pays attention to the distribution diversity inside
each constructed pretext few-shot task when using data augmentation.
Importantly, we highlight the value and importance of the distribution
diversity in the augmentation-based pretext few-shot tasks, which can
effectively alleviate the overfitting problem and make the few-shot model learn
more robust feature representations. In ULDA, we systemically investigate the
effects of different augmentation techniques and propose to strengthen the
distribution diversity (or difference) between the query set and support set in
each few-shot task, by augmenting these two sets diversely (i.e., distribution
shifting). In this way, even incorporated with simple augmentation techniques
(e.g., random crop, color jittering, or rotation), our ULDA can produce a
significant improvement. In the experiments, few-shot models learned by ULDA
can achieve superior generalization performance and obtain state-of-the-art
results in a variety of established few-shot learning tasks on Omniglot and
miniImageNet. The source code is available in
this https URL

本文提出了一种名为 ULDA 的无监督少样本学习框架，通过数据增强中的分布偏移建立预文本少样本任务，重视分布多样性，扩展训练而实现良好的泛化性能并获得了既定少样本学习任务中的最新结果。