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

We present a simple regularization of adversarial perturbations based upon
the perceptual loss. While the resulting perturbations remain imperceptible to
the human eye, they differ from existing adversarial perturbations in that they
are semi-sparse alterations that highlight objects and regions of interest
while leaving the background unaltered. As a semantically meaningful adverse
perturbations, it forms a bridge between counterfactual explanations and
adversarial perturbations in the space of images. We evaluate our approach on
several standard explainability benchmarks, namely, weak localization,
insertion deletion, and the pointing game demonstrating that perceptually
regularized counterfactuals are an effective explanation for image-based
classifiers.

本文提出了一种基于视觉损失的对敌对扰动的简单正则化方法，用于解释图像分类器中的图像分类。这种半稀疏的扰动可以突出对象和感兴趣区域，使其与背景明显不同。通过在多个可解释性基准测试中进行评估，包括局部化、插入删除和指向游戏，证明了感知正则化反事实是图像分类器的有效解释。