Title: Leveraging Perceptual Scores for Dataset Pruning in Computer Vision Tasks Show Chinese title

Title: 利用感知评分进行计算机视觉任务中的数据集剪枝

Abstract: In this paper we propose a score of an image to use for coreset selection in image classification and semantic segmentation tasks. The score is the entropy of an image as approximated by the bits-per-pixel of its compressed version. Thus the score is intrinsic to an image and does not require supervision or training. It is very simple to compute and readily available as all images are stored in a compressed format. The motivation behind our choice of score is that most other scores proposed in literature are expensive to compute. More importantly, we want a score that captures the perceptual complexity of an image. Entropy is one such measure, images with clutter tend to have a higher entropy. However sampling only low entropy iconic images, for example, leads to biased learning and an overall decrease in test performance with current deep learning models. To mitigate the bias we use a graph based method that increases the spatial diversity of the selected samples. We show that this simple score yields good results, particularly for semantic segmentation tasks. Show Chinese abstract

Abstract: 在本文中，我们提出了一种图像的得分，用于图像分类和语义分割任务中的核心集选择。该得分是通过其压缩版本的每像素位数近似得到的图像熵。因此，该得分是图像固有的，不需要监督或训练。它非常容易计算，并且易于获取，因为所有图像都以压缩格式存储。我们选择该得分的动机是，文献中提出的大多数其他得分计算成本较高。更重要的是，我们希望一个能够捕捉图像感知复杂度的得分。熵就是这样一个度量，杂乱的图像往往具有更高的熵。然而，仅采样低熵的标志性图像会导致学习偏差，并且当前深度学习模型的测试性能总体下降。为了减轻偏差，我们使用了一种基于图的方法，以增加所选样本的空间多样性。我们证明了这种简单的得分效果良好，尤其是在语义分割任务中。