Image-Text pretraining on web-scale image caption dataset has become the
default recipe for open vocabulary classification and retrieval models thanks
to the success of CLIP and its variants. Several works have also used CLIP
features for dense prediction tasks and have shown the emergence of open-set
abilities. However, the contrastive objective only focuses on image-text
alignment and does not incentivise image feature learning for dense prediction
tasks. In this work, we propose the simple addition of local-to-global
correspondence learning by self-distillation as an additional objective for
contrastive pre-training to propose SILC. We show that distilling local image
features from an exponential moving average (EMA) teacher model significantly
improves model performance on several computer vision tasks including
classification, retrieval, and especially segmentation. We further show that
SILC scales better with the same training duration compared to the baselines.
Our model SILC sets a new state of the art for zero-shot classification, few
shot classification, image and text retrieval, zero-shot segmentation, and open
vocabulary segmentation.

基于对 CLIP 模型的改进，本研究提出了 SILC 方法，通过引入本地到全局对应学习来预训练模型，有效提升了计算机视觉领域中的分类、检索和分割等任务的性能，取得了零样本分类、少样本分类、图像与文本检索、无样本分割以及开放词汇分割等方面的最新技术成果。

SILC：用自我蒸馏提升视觉语言预训练

SILC: Improving Vision Language Pretraining with Self-Distillation

We propose a learning approach to corner detection for event-based cameras
that is stable even under fast and abrupt motions. Event-based cameras offer
high temporal resolution, power efficiency, and high dynamic range. However,
the properties of event-based data are very different compared to standard
intensity images, and simple extensions of corner detection methods designed
for these images do not perform well on event-based data. We first introduce an
efficient way to compute a time surface that is invariant to the speed of the
objects. We then show that we can train a Random Forest to recognize events
generated by a moving corner from our time surface. Random Forests are also
extremely efficient, and therefore a good choice to deal with the high capture
frequency of event-based cameras ---our implementation processes up to 1.6Mev/s
on a single CPU. Thanks to our time surface formulation and this learning
approach, our method is significantly more robust to abrupt changes of
direction of the corners compared to previous ones. Our method also naturally
assigns a confidence score for the corners, which can be useful for
postprocessing. Moreover, we introduce a high-resolution dataset suitable for
quantitative evaluation and comparison of corner detection methods for
event-based cameras. We call our approach SILC, for Speed Invariant Learned
Corners, and compare it to the state-of-the-art with extensive experiments,
showing better performance.

本研究提出了一种面向基于事件的相机的角点检测方法，通过学习随着快速变化的运动仍然具有稳定性的时间表面，应用随机森林检测由移动角产生的事件，并引入了一个高分辨率的数据集进行方法的可比性验证，实验结果表明，该方法较之前的方法在角点变化方向突然改变时更为稳健，且具有较好的性能表现。