We introduce S$^2$VS, a video similarity learning approach with
self-supervision. Self-Supervised Learning (SSL) is typically used to train
deep models on a proxy task so as to have strong transferability on target
tasks after fine-tuning. Here, in contrast to prior work, SSL is used to
perform video similarity learning and address multiple retrieval and detection
tasks at once with no use of labeled data. This is achieved by learning via
instance-discrimination with task-tailored augmentations and the widely used
InfoNCE loss together with an additional loss operating jointly on
self-similarity and hard-negative similarity. We benchmark our method on tasks
where video relevance is defined with varying granularity, ranging from video
copies to videos depicting the same incident or event. We learn a single
universal model that achieves state-of-the-art performance on all tasks,
surpassing previously proposed methods that use labeled data. The code and
pretrained models are publicly available at:
https://github.com/gkordo/s2vs

介绍了一种名为 S$^2$VS 的视频相似度学习方法，采用自我监督学习来处理多个检索和检测任务，使用任务定制的增强和 InfoNCE 损失，同时学习自相似性和硬负相似性，具有与有标注数据方法相媲美的性能，在视频相关性定义的不同颗粒度上均达到了最先进水平。

自监控的视频相似度学习

Self-Supervised Video Similarity Learning

Leading graph contrastive learning (GCL) methods perform graph augmentations
in two fashions: (1) randomly corrupting the anchor graph, which could cause
the loss of semantic information, or (2) using domain knowledge to maintain
salient features, which undermines the generalization to other domains. Taking
an invariance look at GCL, we argue that a high-performing augmentation should
preserve the salient semantics of anchor graphs regarding
instance-discrimination. To this end, we relate GCL with invariant rationale
discovery, and propose a new framework, Rationale-aware Graph Contrastive
Learning (RGCL). Specifically, without supervision signals, RGCL uses a
rationale generator to reveal salient features about graph
instance-discrimination as the rationale, and then creates rationale-aware
views for contrastive learning. This rationale-aware pre-training scheme endows
the backbone model with the powerful representation ability, further
facilitating the fine-tuning on downstream tasks. On MNIST-Superpixel and MUTAG
datasets, visual inspections on the discovered rationales showcase that the
rationale generator successfully captures the salient features (i.e.
distinguishing semantic nodes in graphs). On biochemical molecule and social
network benchmark datasets, the state-of-the-art performance of RGCL
demonstrates the effectiveness of rationale-aware views for contrastive
learning. Our codes are available at this https URL

提出了一种新的无监督学习框架 RGCL，以图像实例判别的重点语义为目标，使用对抗的方式生成有机理的视图进行对比学习。在 MNIST-Superpixel 和 MUTAG 数据集上的实验结果表明，RGCL 具有较高的表现，有效地为后续任务的优化提供了强大的表示能力。

通过不变量原理发现，激励图对比学习

Let Invariant Rationale Discovery Inspire Graph Contrastive Learning

Clustering is one of the most fundamental tasks in machine learning.
Recently, deep clustering has become a major trend in clustering techniques.
Representation learning often plays an important role in the effectiveness of
deep clustering, and thus can be a principal cause of performance degradation.
In this paper, we propose a clustering-friendly representation learning method
using instance discrimination and feature decorrelation. Our
deep-learning-based representation learning method is motivated by the
properties of classical spectral clustering. Instance discrimination learns
similarities among data and feature decorrelation removes redundant correlation
among features. We utilize an instance discrimination method in which learning
individual instance classes leads to learning similarity among instances.
Through detailed experiments and examination, we show that the approach can be
adapted to learning a latent space for clustering. We design novel
softmax-formulated decorrelation constraints for learning. In evaluations of
image clustering using CIFAR-10 and ImageNet-10, our method achieves accuracy
of 81.5% and 95.4%, respectively. We also show that the softmax-formulated
constraints are compatible with various neural networks.

本研究提出一种基于深度学习的表示学习方法，采用实例区分和特征装饰来提高聚类的效果，在 CIFAR-10 和 ImageNet-10 数据集中进行了实验，分别获得了 81.5% 和 95.4% 的准确率。