1 code implementation • 11 Oct 2023 • Tim Lebailly, Thomas Stegmüller, Behzad Bozorgtabar, Jean-Philippe Thiran, Tinne Tuytelaars
Leveraging nearest neighbor retrieval for self-supervised representation learning has proven beneficial with object-centric images.
2 code implementations • CVPR 2023 • Thomas Stegmüller, Tim Lebailly, Behzad Bozorgtabar, Tinne Tuytelaars, Jean-Philippe Thiran
More importantly, the clustering algorithm conjointly operates on the features of both views, thereby elegantly bypassing the issue of content not represented in both views and the ambiguous matching of objects from one crop to the other.
Ranked #11 on Unsupervised Semantic Segmentation on COCO-Stuff-27
1 code implementation • ICCV 2023 • Tim Lebailly, Thomas Stegmüller, Behzad Bozorgtabar, Jean-Philippe Thiran, Tinne Tuytelaars
Most self-supervised methods for representation learning leverage a cross-view consistency objective i. e., they maximize the representation similarity of a given image's augmented views.
1 code implementation • 29 Jul 2022 • Tim Lebailly, Tinne Tuytelaars
The downstream accuracy of self-supervised methods is tightly linked to the proxy task solved during training and the quality of the gradients extracted from it.
1 code implementation • 6 Oct 2020 • Tim Lebailly, Sena Kiciroglu, Mathieu Salzmann, Pascal Fua, Wei Wang
We argue that the diverse temporal scales are important as they allow us to look at the past frames with different receptive fields, which can lead to better predictions.