Existing models often leverage co-occurrences between objects and their
context to improve recognition accuracy. However, strongly relying on context
risks a model's generalizability, especially when typical co-occurrence
patterns are absent. This work focuses on addressing such contextual biases to
improve the robustness of the learnt feature representations. Our goal is to
accurately recognize a category in the absence of its context, without
compromising on performance when it co-occurs with context. Our key idea is to
decorrelate feature representations of a category from its co-occurring
context. We achieve this by learning a feature subspace that explicitly
represents categories occurring in the absence of context along side a joint
feature subspace that represents both categories and context. Our very simple
yet effective method is extensible to two multi-label tasks -- object and
attribute classification. On 4 challenging datasets, we demonstrate the
effectiveness of our method in reducing contextual bias.

通过学习两个特征子空间，分别表示没有上下文环境的类别和同时表示类别和上下文环境的联合特征子空间，以减少上下文偏差，提高分类准确度，适用于目标和属性两项任务。

不要以物品所处环境论其价值：学习克服语境偏见

Don't Judge an Object by Its Context: Learning to Overcome Contextual  Bias

Visual-semantic embedding models have been recently proposed and shown to be
effective for image classification and zero-shot learning, by mapping images
into a continuous semantic label space. Although several approaches have been
proposed for single-label embedding tasks, handling images with multiple labels
(which is a more general setting) still remains an open problem, mainly due to
the complex underlying corresponding relationship between image and its labels.
In this work, we present Multi-Instance visual-semantic Embedding model (MIE)
for embedding images associated with either single or multiple labels. Our
model discovers and maps semantically-meaningful image subregions to their
corresponding labels. And we demonstrate the superiority of our method over the
state-of-the-art on two tasks, including multi-label image annotation and
zero-shot learning.

通过 Multi-Instance visual-semantic Embedding model (MIE) 模型，我们可以发现和映射成对应标签的语义意义的图像子区域，有效提升了多标签图像注释和零样本学习任务的性能。