Unsupervised multi-object segmentation has shown impressive results on images
by utilizing powerful semantics learned from self-supervised pretraining. An
additional modality such as depth or motion is often used to facilitate the
segmentation in video sequences. However, the performance improvements observed
in synthetic sequences, which rely on the robustness of an additional cue, do
not translate to more challenging real-world scenarios. In this paper, we
propose the first fully unsupervised method for segmenting multiple objects in
real-world sequences. Our object-centric learning framework spatially binds
objects to slots on each frame and then relates these slots across frames. From
these temporally-aware slots, the training objective is to reconstruct the
middle frame in a high-level semantic feature space. We propose a masking
strategy by dropping a significant portion of tokens in the feature space for
efficiency and regularization. Additionally, we address over-clustering by
merging slots based on similarity. Our method can successfully segment multiple
instances of complex and high-variety classes in YouTube videos.

本文提出了第一个完全无监督的方法，用于在真实场景的序列中分割多个对象，通过空间绑定对象并将这些槽联系起来，在高级语义特征空间中重建中间帧，解决了效率和规范化问题，成功地在 YouTube 视频中分割了复杂且多变的类别的多个实例。

视频的自监督目标中心学习

Self-supervised Object-Centric Learning for Videos

It is a consensus that small models perform quite poorly under the paradigm
of self-supervised contrastive learning. Existing methods usually adopt a large
off-the-shelf model to transfer knowledge to the small one via distillation.
Despite their effectiveness, distillation-based methods may not be suitable for
some resource-restricted scenarios due to the huge computational expenses of
deploying a large model. In this paper, we study the issue of training
self-supervised small models without distillation signals. We first evaluate
the representation spaces of the small models and make two non-negligible
observations: (i) the small models can complete the pretext task without
overfitting despite their limited capacity and (ii) they universally suffer the
problem of over clustering. Then we verify multiple assumptions that are
considered to alleviate the over-clustering phenomenon. Finally, we combine the
validated techniques and improve the baseline performances of five small
architectures with considerable margins, which indicates that training small
self-supervised contrastive models is feasible even without distillation
signals. The code is available at
\textit{this https URL}.

本文提出一种无需蒸馏信号即可训练自我监督小模型的方法，验证了小模型可以完成预文本任务且避免过拟合，但会普遍遭受过聚类问题，并提出了多种缓解过聚类的假设，通过验证技术的组合可在五种小型结构上达到基线性能的改进。

关于小型自监督对比模型的有效性研究：不使用蒸馏信号

On the Efficacy of Small Self-Supervised Contrastive Models without  Distillation Signals

Self-supervised learning (especially contrastive learning) has attracted
great interest due to its huge potential in learning discriminative
representations in an unsupervised manner. Despite the acknowledged successes,
existing contrastive learning methods suffer from very low learning efficiency,
e.g., taking about ten times more training epochs than supervised learning for
comparable recognition accuracy. In this paper, we reveal two contradictory
phenomena in contrastive learning that we call under-clustering and
over-clustering problems, which are major obstacles to learning efficiency.
Under-clustering means that the model cannot efficiently learn to discover the
dissimilarity between inter-class samples when the negative sample pairs for
contrastive learning are insufficient to differentiate all the actual object
classes. Over-clustering implies that the model cannot efficiently learn
features from excessive negative sample pairs, forcing the model to
over-cluster samples of the same actual classes into different clusters. To
simultaneously overcome these two problems, we propose a novel self-supervised
learning framework using a truncated triplet loss. Precisely, we employ a
triplet loss tending to maximize the relative distance between the positive
pair and negative pairs to address the under-clustering problem; and we
construct the negative pair by selecting a negative sample deputy from all
negative samples to avoid the over-clustering problem, guaranteed by the
Bernoulli Distribution model. We extensively evaluate our framework in several
large-scale benchmarks (e.g., ImageNet, SYSU-30k, and COCO). The results
demonstrate our model's superiority (e.g., the learning efficiency) over the
latest state-of-the-art methods by a clear margin. Codes available at:
this https URL .

本文研究了自监督学习中对比度学习的效率问题，提出了截断三元组的损失函数以同时解决聚类不足和聚类过度问题，并在多个基准测试中表示其优越性。

自监督表示学习低效性问题的解决

Solving Inefficiency of Self-supervised Representation Learning

Dirichlet process mixture (DPM) models tend to produce many small clusters
regardless of whether they are needed to accurately characterize the data -
this is particularly true for large data sets. However, interpretability,
parsimony, data storage and communication costs all are hampered by having
overly many clusters. We propose a powered Chinese restaurant process to limit
this kind of problem and penalize over clustering. The method is illustrated
using some simulation examples and data with large and small sample size
including MNIST and the Old Faithful Geyser data.

本研究提出一种 “Powered Chinese Restaurant Process” 来防止过度聚类，减少数据的存储成本和沟通成本。