Dec 5, 2022 · In this work, we pretrain network with a location-aware (LOCA) self-supervised method which fosters the emergence of strong dense features.

LOCA is a self-supervised pretraining method which combines relative position and patch-level cluster prediction. This achieves improved transfer on semantic ...

In this work, we pretrain networks with a location-aware (LOCA) self-supervised method which fosters the emergence of strong dense features.

LOCA is a self-supervised pretraining method which combines relative position and patch-level cluster prediction. This achieves improved transfer on semantic ...

Location-Aware Self-Supervised Transformers for Semantic Segmentation Mathilde Caron Neil Houlsby Cordelia Schmid Google Research Abstract

Dec 5, 2022 · In this work, we propose to pretrain networks for semantic segmentation by predicting the relative location of image parts. We formulate this ...

In this work, we pretrain network with a location-aware (LOCA) self-supervised method which fos- ters the emergence of strong dense features. Specifically, we ...

We compare our self-supervised location-aware pretraining to two powerful image-level pretraining paradigms: (i) im- age classification (i.e. label supervision) ...

In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks ( ...

In this work, a theory of few-shot metal generic surface defect segmentation is introduced to solve these challenges. Simultaneously, the Triplet-Graph ...