Zero-shot learning (ZSL) is commonly used to address the very pervasive
problem of predicting unseen classes in fine-grained image classification and
other tasks. One family of solutions is to learn synthesised unseen visual
samples produced by generative models from auxiliary semantic information, such
as natural language descriptions. However, for most of these models,
performance suffers from noise in the form of irrelevant image backgrounds.
Further, most methods do not allocate a calculated weight to each semantic
patch. Yet, in the real world, the discriminative power of features can be
quantified and directly leveraged to improve accuracy and reduce computational
complexity. To address these issues, we propose a novel framework called
multi-patch generative adversarial nets (MPGAN) that synthesises local patch
features and labels unseen classes with a novel weighted voting strategy. The
process begins by generating discriminative visual features from noisy text
descriptions for a set of predefined local patches using multiple specialist
generative models. The features synthesised from each patch for unseen classes
are then used to construct an ensemble of diverse supervised classifiers, each
corresponding to one local patch. A voting strategy averages the probability
distributions output from the classifiers and, given that some patches are more
discriminative than others, a discrimination-based attention mechanism helps to
weight each patch accordingly. Extensive experiments show that MPGAN has
significantly greater accuracy than state-of-the-art methods.

使用生成模型从文本描述中生成区域特征以及使用加权投票策略的修正。