  Recent progress in deterministic prompt learning has become a promising
alternative to various downstream vision tasks, enabling models to learn
powerful visual representations with the help of pre-trained vision-language
models. However, this approach results in limited performance for dense
prediction tasks that require handling more complex and diverse objects, since
a single and deterministic description cannot sufficiently represent the entire
image. In this paper, we present a novel probabilistic prompt learning to fully
exploit the vision-language knowledge in dense prediction tasks. First, we
introduce learnable class-agnostic attribute prompts to describe universal
attributes across the object class. The attributes are combined with class
information and visual-context knowledge to define the class-specific textual
distribution. Text representations are sampled and used to guide the dense
prediction task using the probabilistic pixel-text matching loss, enhancing the
stability and generalization capability of the proposed method. Extensive
experiments on different dense prediction tasks and ablation studies
demonstrate the effectiveness of our proposed method.
