Humans naturally decompose their environment into entities at the appropriate
level of abstraction to act in the world. Allowing machine learning algorithms
to derive this decomposition in an unsupervised way has become an important
line of research. However, current methods are restricted to simulated data or
require additional information in the form of motion or depth in order to
successfully discover objects. In this work, we overcome this limitation by
showing that reconstructing features from models trained in a self-supervised
manner is a sufficient training signal for object-centric representations to
arise in a fully unsupervised way. Our approach, DINOSAUR, significantly
out-performs existing image-based object-centric learning models on simulated
data and is the first unsupervised object-centric model that scales to
real-world datasets such as COCO and PASCAL VOC. DINOSAUR is conceptually
simple and shows competitive performance compared to more involved pipelines
from the computer vision literature.

该研究提出了一种无监督学习的方法，即利用自监督训练的模型来重构特征，从而在完全无监督的情况下出现物体为中心的表示形式，在模拟数据和实际数据集上表现良好，显示出竞争力。

连接现实世界物体中心学习的鸿沟

Bridging the Gap to Real-World Object-Centric Learning

Reinforcement Learning (RL) agents are often unable to generalise well to
environment variations in the state space that were not observed during
training. This issue is especially problematic for image-based RL, where a
change in just one variable, such as the background colour, can change many
pixels in the image. The changed pixels can lead to drastic changes in the
agent's latent representation of the image, causing the learned policy to fail.
To learn more robust representations, we introduce TEmporal Disentanglement
(TED), a self-supervised auxiliary task that leads to disentangled image
representations exploiting the sequential nature of RL observations. We find
empirically that RL algorithms utilising TED as an auxiliary task adapt more
quickly to changes in environment variables with continued training compared to
state-of-the-art representation learning methods. Since TED enforces a
disentangled structure of the representation, our experiments also show that
policies trained with TED generalise better to unseen values of variables
irrelevant to the task (e.g. background colour) as well as unseen values of
variables that affect the optimal policy (e.g. goal positions).

通过引入时间分离（TED）作为强化学习辅助任务，可以更好地利用序列化 RL 观察结果，学习更健壮的表示形式，从而使 RL 算法对未知环境变量的变化更快地适应。