CVPR 2023 - Probabilistic Prompt Learning for Dense Prediction

In this episode we discuss Probabilistic Prompt Learning for Dense Prediction by Hyeongjun Kwon, Taeyong Song, Somi Jeong, Jin Kim, Jinhyun Jang, Kwanghoon Sohn. This paper proposes a new approach called "probabilistic prompt learning" to improve the performance of dense prediction tasks. The authors introduce learnable class-agnostic attribute prompts to describe universal attributes across object classes, which are combined with class information and visual-context knowledge to create a class-specific textual distribution. Text representations are then sampled and used to guide the dense prediction task using a probabilistic pixel-text matching loss, resulting in improved stability and generalization capabilities. The effectiveness of the proposed method is demonstrated through extensive experiments and ablation studies.