ImageNet项目是一个用于视觉对象识别软件研究的大型可视化数据库。超过1400万的图像URL被ImageNet手动注释,以指示图片中的对象;在至少一百万个图像中,还提供了边界框。ImageNet包含2万多个类别; [2]一个典型的类别,如“气球”或“草莓”,包含数百个图像。第三方图像URL的注释数据库可以直接从ImageNet免费获得;但是,实际的图像不属于ImageNet。自2010年以来,ImageNet项目每年举办一次软件比赛,即ImageNet大规模视觉识别挑战赛(ILSVRC),软件程序竞相正确分类检测物体和场景。 ImageNet挑战使用了一个“修剪”的1000个非重叠类的列表。2012年在解决ImageNet挑战方面取得了巨大的突破,被广泛认为是2010年的深度学习革命的开始。

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该文提出一种简单而有效的方法,无需任何tricks,它可以将标准ResNet50的Top1精度提升到80%+。该方法是基于作者之前MEAL(通过判别方式进行知识蒸馏集成)改进而来,作者对MEAL进行了以下两点改进:

(1) 仅在最后的输出部分使用相似性损失与判别损失;

(2) 采用所有老师模型的平均概率作为更强的监督信息进行蒸馏。

该文提到一个非常重要的发现:在蒸馏阶段不应当使用one-hot方式的标签编码。这样一种简单的方案可以取得SOTA性能,且并未用到以下几种常见涨点tricks:(1)类似ResNet50-D的架构改进;(2)额外训练数据;(3) AutoAug、RandAug等;(4)cosine学习率机制;(5)mixup/cutmix数据增广策略;(6) 标签平滑。

在ImageNet数据集上,本文所提方法取得了80.67%的Top1精度(single crop@224),以极大的优势超越其他同架构方案。该方法可以视作采用知识蒸馏对ResNet50涨点的一个新的基准,该文可谓首个在不改变网路架构、无需额外训练数据的前提下将ResNet提升到超过80%Top1精度的方法。

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Dense correspondence across semantically related images has been extensively studied, but still faces two challenges: 1) large variations in appearance, scale and pose exist even for objects from the same category, and 2) labeling pixel-level dense correspondences is labor intensive and infeasible to scale. Most existing approaches focus on designing various matching approaches with fully-supervised ImageNet pretrained networks. On the other hand, while a variety of self-supervised approaches are proposed to explicitly measure image-level similarities, correspondence matching the pixel level remains under-explored. In this work, we propose a multi-level contrastive learning approach for semantic matching, which does not rely on any ImageNet pretrained model. We show that image-level contrastive learning is a key component to encourage the convolutional features to find correspondence between similar objects, while the performance can be further enhanced by regularizing cross-instance cycle-consistency at intermediate feature levels. Experimental results on the PF-PASCAL, PF-WILLOW, and SPair-71k benchmark datasets demonstrate that our method performs favorably against the state-of-the-art approaches. The source code and trained models will be made available to the public.

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