Title: Multi-instance Interactive Segmentation with Self-Supervised Transformer. Abstract: The rise of Vision Transformers (ViT) combined with better self-supervised learning pre-tasks has taken representation learning to the next level, beating supervised results on ImageNet. In particular, self-attention mechanism of ViT allows to easily visualize semantic information learned by the network. Following revealing of attention maps of DINO, many tried to leverage its representations for unsupervised segmentation. Despite very promising results for basic images with a single clear object in a simple background, representation of ViT are not able to segment images, with several classes and object instance, in an unsupervised fashion yet. In this paper, we propose SALT: Semi-supervised Segmentation with Self-supervised Attention Layers in Transformers, an interactive algorithm for multi-class/multi-instance segmentation. We follow previous works path and take it a step further by discriminating between different objects, using sparse human help to select said objects. We show that remarkable results are achieved with very sparse labels. Different pre-tasks are compared, and we show that self-supervised ones are more robust for panoptic segmentation, and overall achieve very similar performance. Evaluation is carried out on Pascal VOC 2007 and COCO-panoptic. Performance is evaluated for extreme conditions such as very noisy, and sparse interactions going to as little as one interaction per class.