Few-shot open-set recognition aims to classify both seen and novel images
given only limited training data of seen classes. The challenge of this task is
that the model is required not only to learn a discriminative classifier to
classify the pre-defined classes with few training data but also to reject
inputs from unseen classes that never appear at training time. In this paper,
we propose to solve the problem from two novel aspects. First, instead of
learning the decision boundaries between seen classes, as is done in standard
close-set classification, we reserve space for unseen classes, such that images
located in these areas are recognized as the unseen classes. Second, to
effectively learn such decision boundaries, we propose to utilize the
background features from seen classes. As these background regions do not
significantly contribute to the decision of close-set classification, it is
natural to use them as the pseudo unseen classes for classifier learning. Our
extensive experiments show that our proposed method not only outperforms
multiple baselines but also sets new state-of-the-art results on three popular
benchmarks, namely tieredImageNet, miniImageNet, and Caltech-USCD
Birds-200-2011 (CUB).

该论文提出了一种新颖的方法来解决 few-shot open-set recognition 问题；首先，利用 background features 提取 pseudo unseen classes，将决策边界保留给 unseen classes，以更有效地学习分类器；通过对 tieredImageNet、miniImageNet 和 CUB 三个基准数据集的实验，实现了超越多个基准的 state-of-the-art 结果。

利用背景作为未知元素的小样本开放式识别

Few-shot Open-set Recognition Using Background as Unknowns

Human image generation is a very challenging task since it is affected by
many factors. Many human image generation methods focus on generating human
images conditioned on a given pose, while the generated backgrounds are often
blurred.In this paper,we propose a novel Partition-Controlled GAN to generate
human images according to target pose and background. Firstly, human poses in
the given images are extracted, and foreground/background are partitioned for
further use. Secondly, we extract and fuse appearance features, pose features
and background features to generate the desired images. Experiments on
Market-1501 and DeepFashion datasets show that our model not only generates
realistic human images but also produce the human pose and background as we
want. Extensive experiments on COCO and LIP datasets indicate the potential of
our method.

用 Partition-Controlled GAN 生成符合目标姿势和背景的人类图像，模型通过提取和融合外观特征、姿势特征和背景特征来生成期望的图像，实验表明该方法不仅可以生成逼真的人类图像，还能符合预期的人体姿势和背景。