我们的多实例对比学习框架以WSI特征包(WSI-Fbag)作为输入,其中特征包由在ImageNet上预训练的ResNet18[39]提取的实例级嵌入组成。CL的一个关键组成部分是构建用于训练的逻辑正/负对(即语义相似/不相似的实例)。与以往基于图像增强的策略不同,我们提出从每个WSI-Fbag中抽取不同的WSI判别集(简称WSI-Fset),构建基于...
论文标题:Contrastive Learning for Image Captioning 论文来源:NIPS 2017 论文链接:arxiv.org/abs/1710.0253 代码链接:github.com/doubledaibo/ 这篇文章希望通过使用对比学习来解决 image captioning 中标题文本可区别性的问题,即尽可能让标题描述和唯一的一张图片对应,而不是笼统而又模糊的可能和多张图片对应。作者引...
Current image classification methods with supervised learning have achieved good classification accuracy. However, supervised image classification methods mainly focus on the semantic differences at the class level, while lacking attention to the instance level. The core idea of contrastive learning is to ...
上面这个例子来自于 Contrastive Self-supervised Learning[2]这篇 Blog,表达的一个核心思想就是:尽管我们已经见过很多次钞票长什么样子,但我们很少能一模一样的画出钞票;虽然我们画不出栩栩如生的钞票,但我们依旧可以轻易地辨别出钞票。 基于此,也就意味着表示学习算法并不一定要关注到样本的每一个细节,只要学到的...
最近深度学习两巨头 Bengio 和 LeCun 在 ICLR 2020 上点名 Self-Supervised Learning(SSL,自监督学习) 是 AI 的未来,而其的代表的 Framework 便是 Contrastive Learning(CL,对比学习)。 另一巨头 Hinton 和 Kaiming 两尊大神也在这问题上隔空过招,MoCo、SimCLR、MoCo V2 打得火热,这和 BERT 之后,各大公司出...
Recently, self-supervised learning (SSL) has gained great prominence in hyperspectral image classification (HSIC) due to its powerful capability to alleviate data-hunger problem. The generative-based method and the contrastive-based method have become two main streams in the field of SSL. To fully...
Contrastive learning has recently shown high accuracy results in supervised environments where fully labeled images were utilized for image classification. In the e-commerce field, product image datasets tend not to have a large number of instan...
Top: The pre-training stage, which includes data augmentation and representation learning. The pretext task is an instance discrimination task. Bottom: Few-shot classification with 2-way, 1-shot example. For classification, the support images and query image are encoded by the pre-trained encoding...
multiple prototype的思路与multi-head attention类似,应用了之后可能还可以再高几个点。 参考文献: [1] Wang, Peng, et al. "Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification."Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021....
第二,在Feature Learning阶段,作者采用了监督对比学习来学习Feature。在该步骤探索了两种用于特征学习的对比损失,一种是最近提出的有监督对比(SC)损失,在无监督对比损失基础上通过合并来自同一类的正样本。另一种是原型监督对比 (PSC) 学习策略,解决了标准 SC 损失中的密集内存消耗,适用于有限的内存预算。 Method Fea...