When training samples are scarce, the semantic embedding technique, ie,
describing class labels with attributes, provides a condition to generate
visual features for unseen objects by transferring the knowledge from seen
objects. However, semantic descriptions are usually obtained in an external
paradigm, such as manual annotation, resulting in weak consistency between
descriptions and visual features. In this paper, we refine the coarse-grained
semantic description for any-shot learning tasks, ie, zero-shot learning (ZSL),
generalized zero-shot learning (GZSL), and few-shot learning (FSL). A new
model, namely, the semantic refinement Wasserstein generative adversarial
network (SRWGAN) model, is designed with the proposed multihead representation
and hierarchical alignment techniques. Unlike conventional methods, semantic
refinement is performed with the aim of identifying a bias-eliminated condition
for disjoint-class feature generation and is applicable in both inductive and
transductive settings. We extensively evaluate model performance on six
benchmark datasets and observe state-of-the-art results for any-shot learning;
eg, we obtain 70.2% harmonic accuracy for the Caltech UCSD Birds (CUB) dataset
and 82.2% harmonic accuracy for the Oxford Flowers (FLO) dataset in the
standard GZSL setting. Various visualizations are also provided to show the
bias-eliminated generation of SRWGAN. Our code is available.

本文提出了一种针对零样本学习、广义零样本学习和少样本学习的语义细化 Wasserstein 生成对抗网络 (SRWGAN) 模型，采用多头表示和分层对齐技术实现语义细化，为分离的类别特征生成提供无偏差的条件，将应用于归纳和转导设置中，并在六个基准数据集上获得了最先进的结果。

消除偏差的语义细化方法用于任意样本学习

Bias-Eliminated Semantic Refinement for Any-Shot Learning

Anomaly detection in surveillance videos has been recently gaining attention.
Even though the performance of state-of-the-art methods on publicly available
data sets has been competitive, they demand a massive amount of training data.
Also, they lack a concrete approach for continuously updating the trained model
once new data is available. Furthermore, online decision making is an important
but mostly neglected factor in this domain. Motivated by these research gaps,
we propose an online anomaly detection method for surveillance videos using
transfer learning and any-shot learning, which in turn significantly reduces
the training complexity and provides a mechanism that can detect anomalies
using only a few labeled nominal examples. Our proposed algorithm leverages the
feature extraction power of neural network-based models for transfer learning
and the any-shot learning capability of statistical detection methods.

本研究提出了一种基于转移学习和任意示例学习的在线异常检测算法，可以使用少量标记的正常示例检测异常，具有训练复杂度低的优点，适用于监控视频中的在线决策。