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。

重新思考生成式零样本学习：一种基于集成学习的视觉图块识别方法

Rethinking Generative Zero-Shot Learning: An Ensemble Learning  Perspective for Recognising Visual Patches

This paper presents a new deep learning approach for video-based scene
classification. We design a Heterogeneous Deep Discriminative Model (HDDM)
whose parameters are initialized by performing an unsupervised pre-training in
a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBM). In
order to avoid the redundancy of adjacent frames, we extract spatiotemporal
variation patterns within frames and represent them sparsely using Sparse Cubic
Symmetrical Pattern (SCSP). Then, a pre-initialized HDDM is separately trained
using the videos of each class to learn class-specific models. According to the
minimum reconstruction error from the learnt class-specific models, a weighted
voting strategy is employed for the classification. The performance of the
proposed method is extensively evaluated on two action recognition datasets;
UCF101 and Hollywood II, and three dynamic texture and dynamic scene datasets;
DynTex, YUPENN, and Maryland. The experimental results and comparisons against
state-of-the-art methods demonstrate that the proposed method consistently
achieves superior performance on all datasets.

本文提出了一种新的深度学习方法来处理基于视频的场景分类问题，使用 HDDM 模型，通过异构无监督预训练和 SCSP 来解决存储问题，并使用加权投票策略进行分类，实验结果表明该方法在所有数据集上都实现了卓越的性能。