Mask-supervised Object-centric Representation Learning with LeJEPA
Self-supervised image encoders deliver strong features for downstream tasks but need many images for training. A natural remedy to counter this is to make each image count for more. A scene contains many objects, and given masks from human annotators or an off-the-shelf segmentation model, pre-training can focus on aligning per-object rather than image-wide representations, extracting more signal from every image. Existing mask-supervised methods do this through reconstruction or contrastive losses that leverage negative objects. We instead use two separate projection spaces for the alignment. In a \emph{semantic space}, per-object representations from different views are aligned. To avoi...
| Authors | Jakob Geusen, Ender Konukoglu |
|---|---|
| Primary category | cs.CV |
| Published | 2 July 2026 |
| Last updated at source | 5 October 2026 |
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