We propose Hierarchical ProtoPNet: an interpretable network that explains its
reasoning process by considering the hierarchical relationship between classes.
Different from previous methods that explain their reasoning process by
dissecting the input image and finding the prototypical parts responsible for
the classification, we propose to explain the reasoning process for video
action classification by dissecting the input video frames on multiple levels
of the class hierarchy. The explanations leverage the hierarchy to deal with
uncertainty, akin to human reasoning: When we observe water and human activity,
but no definitive action it can be recognized as the water sports parent class.
Only after observing a person swimming can we definitively refine it to the
swimming action. Experiments on ActivityNet and UCF-101 show performance
improvements while providing multi-level explanations.

本文提出一种可解释的网络模型 Hierarchical ProtoPNet，该模型通过考虑类别之间的层次关系来解释其推理过程，并在视频动作分类领域中应用，提出了一种多级解释的方法。在 ActivityNet 和 UCF-101 数据集上实验证明该方法能够提高性能。

视频动作识别的分层解释

Hierarchical Explanations for Video Action Recognition

Humans are able to recognize structured relations in observation, allowing us
to decompose complex scenes into simpler parts and abstract the visual world in
multiple levels. However, such hierarchical reasoning ability of human
perception remains largely unexplored in current literature of semantic
segmentation. Existing work is often aware of flatten labels and predicts
target classes exclusively for each pixel. In this paper, we instead address
hierarchical semantic segmentation (HSS), which aims at structured, pixel-wise
description of visual observation in terms of a class hierarchy. We devise
HSSN, a general HSS framework that tackles two critical issues in this task: i)
how to efficiently adapt existing hierarchy-agnostic segmentation networks to
the HSS setting, and ii) how to leverage the hierarchy information to
regularize HSS network learning. To address i), HSSN directly casts HSS as a
pixel-wise multi-label classification task, only bringing minimal architecture
change to current segmentation models. To solve ii), HSSN first explores
inherent properties of the hierarchy as a training objective, which enforces
segmentation predictions to obey the hierarchy structure. Further, with
hierarchy-induced margin constraints, HSSN reshapes the pixel embedding space,
so as to generate well-structured pixel representations and improve
segmentation eventually. We conduct experiments on four semantic segmentation
datasets (i.e., Mapillary Vistas 2.0, Cityscapes, LIP, and PASCAL-Person-Part),
with different class hierarchies, segmentation network architectures and
backbones, showing the generalization and superiority of HSSN.

本文提出了一种用于分层语义分割的新架构 HSSN，它可将现有的分割网络有效地应用于分层语义分割，通过像素嵌入和类别层次结构来实现像素级多标签分类和更好的分割。

深层次语义分割

Deep Hierarchical Semantic Segmentation

Semantic embedding has been widely investigated for aligning knowledge graph
(KG) entities. Current methods have explored and utilized the graph structure,
the entity names and attributes, but ignore the ontology (or ontological
schema) which contains critical meta information such as classes and their
membership relationships with entities. In this paper, we propose an
ontology-guided entity alignment method named OntoEA, where both KGs and their
ontologies are jointly embedded, and the class hierarchy and the class
disjointness are utilized to avoid false mappings. Extensive experiments on
seven public and industrial benchmarks have demonstrated the state-of-the-art
performance of OntoEA and the effectiveness of the ontologies.

本文提出一个以本体指导实体对齐的方法 OntoEA，该方法通过联合嵌入本体和知识图谱来避免虚假映射，实验证明了该方法的优越性和本体的有效性。

OntoEA: 基于本体引导的实体对齐方法，通过知识图谱联合嵌入

OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph  Embedding

Insufficient or even unavailable training data of emerging classes is a big
challenge of many classification tasks, including text classification.
Recognising text documents of classes that have never been seen in the learning
stage, so-called zero-shot text classification, is therefore difficult and only
limited previous works tackled this problem. In this paper, we propose a
two-phase framework together with data augmentation and feature augmentation to
solve this problem. Four kinds of semantic knowledge (word embeddings, class
descriptions, class hierarchy, and a general knowledge graph) are incorporated
into the proposed framework to deal with instances of unseen classes
effectively. Experimental results show that each and the combination of the two
phases achieve the best overall accuracy compared with baselines and recent
approaches in classifying real-world texts under the zero-shot scenario.

本文提出了一个两阶段的框架，结合了数据扩充和特征扩充来解决零样本文本分类问题，同时将四种语义知识结构（词嵌入，类别描述，类层次结构和通用知识图谱）纳入到该框架中，实验结果表明，该框架的单独和联合两个阶段相比基线和最近的方法均获得了最佳的整体准确率。