  Recently, CLIP-based approaches have exhibited remarkable performance on
generalization and few-shot learning tasks, fueled by the power of contrastive
language-vision pre-training. In particular, prompt tuning has emerged as an
effective strategy to adapt the pre-trained language-vision models to
downstream tasks by employing task-related textual tokens. Motivated by this
progress, in this work we question whether other fundamental problems, such as
weakly supervised semantic segmentation (WSSS), can benefit from prompt tuning.
Our findings reveal two interesting observations that shed light on the impact
of prompt tuning on WSSS. First, modifying only the class token of the text
prompt results in a greater impact on the Class Activation Map (CAM), compared
to arguably more complex strategies that optimize the context. And second, the
class token associated with the image ground truth does not necessarily
correspond to the category that yields the best CAM. Motivated by these
observations, we introduce a novel approach based on a PrOmpt cLass lEarning
(POLE) strategy. Through extensive experiments we demonstrate that our simple,
yet efficient approach achieves SOTA performance in a well-known WSSS
benchmark. These results highlight not only the benefits of language-vision
models in WSSS but also the potential of prompt learning for this problem. The
code is available at https://github.com/rB080/WSS_POLE.
