Self-Supervised learning (SSL) with Joint-Embedding Architectures (JEA) has
led to outstanding performances. All instantiations of this paradigm were
trained using strong and well-established hand-crafted data augmentations,
leading to the general belief that they are required for the proper training
and performance of such models. On the other hand, generative
reconstruction-based models such as BEIT and MAE or Joint-Embedding Predictive
Architectures such as I-JEPA have shown strong performance without using data
augmentations except masking. In this work, we challenge the importance of
invariance and data-augmentation in JEAs at scale. By running a case-study on a
recent SSL foundation model - DINOv2 - we show that strong image
representations can be obtained with JEAs and only cropping without resizing
provided the training data is large enough, reaching state-of-the-art results
and using the least amount of augmentation in the literature. Through this
study, we also discuss the impact of compute constraints on the outcomes of
experimental deep learning research, showing that they can lead to very
different conclusions.

本研究探讨在大规模数据集下只使用裁剪而不使用调整大小的数据增强可得到强大的图像表示，并达到最先进的结果，同时讨论计算约束对实验深度学习研究结果的影响。

自监督学习中不需要数据增强

You Don't Need Data-Augmentation in Self-Supervised Learning

Rapid advancements in continual segmentation have yet to bridge the gap of
scaling to large continually expanding vocabularies under compute-constrained
scenarios. We discover that traditional continual training leads to
catastrophic forgetting under compute constraints, unable to outperform
zero-shot segmentation methods. We introduce a novel strategy for semantic and
panoptic segmentation with zero forgetting, capable of adapting to continually
growing vocabularies without the need for retraining or large memory costs. Our
training-free approach, kNN-CLIP, leverages a database of instance embeddings
to enable open-vocabulary segmentation approaches to continually expand their
vocabulary on any given domain with a single-pass through data, while only
storing embeddings minimizing both compute and memory costs. This method
achieves state-of-the-art mIoU performance across large-vocabulary semantic and
panoptic segmentation datasets. We hope kNN-CLIP represents a step forward in
enabling more efficient and adaptable continual segmentation, paving the way
for advances in real-world large-vocabulary continual segmentation methods.

我们引入了一种新颖的连续语义和全景分割策略，名为 kNN-CLIP，它能够在计算和内存成本最小化的情况下，实现对不断扩大的词汇的适应，并在大词汇语义和全景分割数据集上取得了最先进的性能表现，为实现更高效、可适应性强的连续分割方法迈出了一步。