  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 to achieve strong performance not only on
novel categories with limited examples, but also to retain performance on base
categories. We propose an approach to learn visual prompts with limited
examples. These learned visual prompts are used to prompt a multiscale
transformer decoder to facilitate accurate dense predictions. Additionally, we
introduce a unidirectional causal attention mechanism between the novel
prompts, learned with limited examples, and the base prompts, learned with
abundant data. This mechanism enriches the novel prompts without deteriorating
the base class performance. Overall, this form of prompting helps us achieve
state-of-the-art performance for GFSS on two different benchmark datasets:
COCO-$20^i$ and Pascal-$5^i$, without the need for test-time optimization (or
transduction). Furthermore, test-time optimization leveraging unlabelled test
data can be used to improve the prompts, which we refer to as transductive
prompt tuning.
