語義分割的主動學習: 區域多樣性和動態域密度

Abstract

雖然深度學習在監督語義分割方面取得了成功，獲得大規模的人工標註仍然具有挑戰性。在這種情況下，用少量訊息量大的標註資料最大化模型效能的主動 學習便能派上用場。在本論文中，我們分別提出了一個用於單域和跨域語義分割的通用主動學習框架。對於單域問題，我們設計了一種區域多樣性主動學習策略， 以最大限度地減少三維點雲語義分割的人工標註工作;針對領域自適應問題，我們提出了一種動態域密度主動域自適應方法，大幅減少了目標域標註的數量。大量實驗顯示，我們的方法高度優於以前的主動學習策略。此外，我們的方法可以 在多個常用數據集上以不到 15% 的標註達到完全監督學習的 90% 以上效能。

Despite the success of deep learning on supervised semantic segmentation, obtaining large-scale manual annotations is still challenging. Active learning, maximizing model performance with few informative labeled data, comes in handy for such a scenario. In this thesis, we present a general active learning framework for single-domain and cross-domain semantic segmentation, respectively. For single-domain problems, we design a regional diversity active learning strategy to minimize manual labeling effort for 3D point cloud semantic segmentation; for domain adaptation problems, we propose a dynamic domain density active domain adaptation method, greatly reducing the number of target domain annotations. Extensive experiments show that our method highly outperforms previous active learning strategies. Moreover, our method can achieve over 90% performance of fully supervised learning with less than 15% annotations on multiple commonly used datasets.