掩码改善卷积神经网络的对比自监督学习，且显著性告诉你掩码位置
Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You Where
AI总结：
本文针对卷积神经网络对比学习中随机掩码可能过度覆盖显著对象的问题，提出引入显著性约束使掩码在前景和背景间均匀分布，并通过掩码大区域显著块构造困难负样本，在多个数据集和任务上验证了方法的优越性。
摘要：
虽然图像数据由于分词过程和视觉Transformer骨干的引入，开始受益于建立在掩码和自重建目标之上的简单而有效的自监督学习方案，但卷积神经网络作为图像数据的另一种重要且被广泛采用的架构，尽管有对比学习技术来驱动自监督学习，却仍然难以利用这种直接且通用的掩码操作来显著提升其学习过程。在这项工作中，我们旨在减轻将掩码操作作为额外增强方法纳入卷积神经网络对比学习框架的负担。除了先前工作已经讨论过的由掩码操作对卷积神经网络造成的附加但不希望的边缘（掩码区域与未掩码区域之间）以及其他不利影响之外，我们特别识别出一个潜在问题：在对比样本对的一个视图中，随机采样的掩码区域可能过度集中在重要或显著的对象上，从而导致对另一个视图产生误导性的对比性。为此，我们提出显式地考虑显著性约束，使掩码区域在前景和背景之间更均匀地分布，以实现基于掩码的增强。此外，我们通过在输入图像中掩码更大区域的显著图像块来引入困难负样本。在各种数据集、对比学习机制和下游任务上进行的大量实验充分验证了我们所提方法相对于多个最先进基线的有效性和优越性能。
英文摘要：
While image data starts to enjoy the simple-but-effective self-supervised learning scheme built upon masking and self-reconstruction objective thanks to the introduction of tokenization procedure and vision transformer backbone, convolutional neural networks as another important and widely-adopted architecture for image data, though having contrastive-learning techniques to drive the self-supervised learning, still face the difficulty of leveraging such straightforward and general masking operation to benefit their learning process significantly. In this work, we aim to alleviate the burden of including masking operation into the contrastive-learning framework for convolutional neural networks as an extra augmentation method. In addition to the additive but unwanted edges (between masked and unmasked regions) as well as other adverse effects caused by the masking operations for ConvNets, which have been discussed by prior works, we particularly identify the potential problem where for one view in a contrastive sample-pair the randomly-sampled masking regions could be overly concentrated on important/salient objects thus resulting in misleading contrastiveness to the other view. To this end, we propose to explicitly take the saliency constraint into consideration in which the masked regions are more evenly distributed among the foreground and background for realizing the masking-based augmentation. Moreover, we introduce hard negative samples by masking larger regions of salient patches in an input image. Extensive experiments conducted on various datasets, contrastive learning mechanisms, and downstream tasks well verify the efficacy as well as the superior performance of our proposed method with respect to several state-of-the-art baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
