Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approach

arXiv:2404.11732v1 Announce Type: new
Abstract: The emergence of attention-based transformer models has led to their extensive use in various tasks, due to their superior generalization and transfer properties. Recent research has demonstrated that such models, when prompted appropriately, are excellent for few-shot inference. However, such techniques are under-explored for dense prediction tasks like semantic segmentation. In this work, we examine the effectiveness of prompting a transformer-decoder with learned visual prompts for the generalized few-shot segmentation (GFSS) task. Our goal is …

abstract arxiv attention cs.cv emergence few-shot generalized however inference prediction prompting research scale segmentation tasks transfer transformer transformer models type visual visual prompting