Multiple categories of objects are present in most images. Treating this as a
multi-class classification is not justified. We treat this as a multi-label
classification problem. In this paper, we further aim to minimize the
supervision required for providing supervision in multi-label classification.
Specifically, we investigate an effective class of approaches that associate a
weak localization with each category either in terms of the bounding box or
segmentation mask. Doing so improves the accuracy of multi-label
categorization. The approach we adopt is one of active learning, i.e.,
incrementally selecting a set of samples that need supervision based on the
current model, obtaining supervision for these samples, retraining the model
with the additional set of supervised samples and proceeding again to select
the next set of samples. A crucial concern is the choice of the set of samples.
In doing so, we provide a novel insight, and no specific measure succeeds in
obtaining a consistently improved selection criterion. We, therefore, provide a
selection criterion that consistently improves the overall baseline criterion
by choosing the top k set of samples for a varied set of criteria. Using this
criterion, we are able to show that we can retain more than 98% of the fully
supervised performance with just 20% of samples (and more than 96% using 10%)
of the dataset on PASCAL VOC 2007 and 2012. Also, our proposed approach
consistently outperforms all other baseline metrics for all benchmark datasets
and model combinations.

本研究探究了在进行多标签分类时，使用带每个类别的弱位置信息的方法来提高分类精度，同时采用主动学习策略，通过选择样本和获得反馈进行逐步监督，结果表明我们的方法与基线相比，在多个基准数据集和模型组合中表现均优秀，用此方法能够在 VOC 2007 和 2012 的数据集上，只使用 20% 的样本就可以保留超过 98% 的完全监督性能。

多标签分类中最小化监督

Minimizing Supervision in Multi-label Categorization

The paper systematically studies the impact of a range of recent advances in
CNN architectures and learning methods on the object categorization (ILSVRC)
problem. The evalution tests the influence of the following choices of the
architecture: non-linearity (ReLU, ELU, maxout, compatibility with batch
normalization), pooling variants (stochastic, max, average, mixed), network
width, classifier design (convolutional, fully-connected, SPP), image
pre-processing, and of learning parameters: learning rate, batch size,
cleanliness of the data, etc.
The performance gains of the proposed modifications are first tested
individually and then in combination. The sum of individual gains is bigger
than the observed improvement when all modifications are introduced, but the
"deficit" is small suggesting independence of their benefits. We show that the
use of 128x128 pixel images is sufficient to make qualitative conclusions about
optimal network structure that hold for the full size Caffe and VGG nets. The
results are obtained an order of magnitude faster than with the standard 224
pixel images.

本文系统地研究了卷积神经网络结构和学习方法的多个进展对目标分类（ILSVRC）问题的影响，并测试了网络结构、学习参数和图像预处理等因素对分类结果的影响。结果表明，使用 128x128 像素图像即可得出优化网络结构的定性结论，这些结果比标准的 224 像素图像快一个数量级。

对 ImageNet 上 CNN 进步的系统评估

Systematic evaluation of CNN advances on the ImageNet

We propose a method that learns a discriminative yet semantic space for
object categorization, where we also embed auxiliary semantic entities such as
supercategories and attributes. Contrary to prior work which only utilized them
as side information, we explicitly embed the semantic entities into the same
space where we embed categories, which enables us to represent a category as
their linear combination. By exploiting such a unified model for semantics, we
enforce each category to be represented by a supercategory + sparse combination
of attributes, with an additional exclusive regularization to learn
discriminative composition.

本研究提出了一种基于语义和区分性的物体分类方法，并采用辅助的语义实体（如超类和属性）来实现，通过将它们都嵌入到相同的语义空间中，使我们能够将分类表示为它们的线性组合，并在学习中引入独特的正则化方法。