Object classes that surround us have a natural tendency to emerge at varying
levels of abstraction. We propose a Bayesian approach to zero-shot learning
(ZSL) that introduces the notion of meta-classes and implements a Bayesian
hierarchy around these classes to effectively blend data likelihood with local
and global priors. Local priors driven by data from seen classes, i.e. classes
that are available at training time, become instrumental in recovering unseen
classes, i.e. classes that are missing at training time, in a generalized ZSL
setting. Hyperparameters of the Bayesian model offer a convenient way to
optimize the trade-off between seen and unseen class accuracy in addition to
guiding other aspects of model fitting. We conduct experiments on seven
benchmark datasets including the large scale ImageNet and show that our model
improves the current state of the art in the challenging generalized ZSL
setting.

本文提出了一种贝叶斯方法，通过引入元类概念和在类周围实现贝叶斯层次结构，将数据似然性与本地和全局先验相结合，从而在归纳式零样本学习中恢复未见过的类。

贝叶斯零样本学习

Bayesian Zero-Shot Learning

Current Zero-Shot Learning (ZSL) approaches are restricted to recognition of
a single dominant unseen object category in a test image. We hypothesize that
this setting is ill-suited for real-world applications where unseen objects
appear only as a part of a complex scene, warranting both the `recognition' and
`localization' of an unseen category. To address this limitation, we introduce
a new \emph{`Zero-Shot Detection'} (ZSD) problem setting, which aims at
simultaneously recognizing and locating object instances belonging to novel
categories without any training examples. We also propose a new experimental
protocol for ZSD based on the highly challenging ILSVRC dataset, adhering to
practical issues, e.g., the rarity of unseen objects. To the best of our
knowledge, this is the first end-to-end deep network for ZSD that jointly
models the interplay between visual and semantic domain information. To
overcome the noise in the automatically derived semantic descriptions, we
utilize the concept of meta-classes to design an original loss function that
achieves synergy between max-margin class separation and semantic space
clustering. Furthermore, we present a baseline approach extended from
recognition to detection setting. Our extensive experiments show significant
performance boost over the baseline on the imperative yet difficult ZSD
problem.

本文提出了用于同时识别和定位未见过类别的物体实例的 Zero-Shot Detection 问题，并引入基于 ILSVRC 数据集的新实验协议和元类概念来改善自动推导的语义描述的噪音，旨在在视觉和语义领域信息之间建立协同作用的新型 'Zero-Shot Detection' 深度神经网络。