基於區域和多尺度物體檢測的主動學習方法

Abstract

取得大規模標註的物體檢測資料集往往耗時且昂貴，因為需要對圖像進行邊界框和類別標籤的標註。為了減少成本，一些專門的主動學習方法被提出，可以從未標註的數據中選擇粗粒度樣本或細粒度實例進行標註。然而，前者的方法容易產生冗餘標註，而後者的方法通常會導致訓練的不穩定性和採樣偏差。為了應對這些挑戰，我們提出了一種名為多尺度基於區域的主動學習（MuRAL）的物體檢測方法。MuRAL通過識別不同尺度的信息區域，減少對已經學習良好的物體進行標註的成本，同時提高訓練性能。信息區域的得分設計考慮了實例的預測置信度和每個物體類別的分佈，使得我們的方法能夠更加關注難以檢測的類別。此外，MuRAL採用了一種尺度感知的選擇策略，確保從不同尺度選擇多樣化的區域進行標註和下游微調，從而增強訓練的穩定性。我們的方法在Cityscapes和MS COCO數據集上超越了所有現有的粗粒度和細粒度基準線，並在困難類別性能上實現了顯著改進。

Obtaining large-scale labeled object detection dataset can be costly and time-consuming, as it involves annotating images with bounding boxes and class labels. Thus, some specialized active learning methods have been proposed to reduce the cost by selecting either coarse-grained samples or fine-grained instances from unlabeled data for labeling. However, the former approaches suffer from redundant labeling, while the latter methods generally lead to training instability and sampling bias. To address these challenges, we propose a novel approach called Multi-scale Region-based Active Learning (MuRAL) for object detection. MuRAL identifies informative regions of various scales to reduce annotation costs for well-learned objects and improve training performance. The informative region score is designed to consider both the predicted confidence of instances and the distribution of each object category, enabling our method to focus more on difficult-to-detect classes. Moreover, MuRAL employs a scale-aware selection strategy that ensures diverse regions are selected from different scales for labeling and downstream finetuning, which enhances training stability. Our proposed method surpasses all existing coarse-grained and fine-grained baselines on Cityscapes and MS COCO datasets, and demonstrates significant improvement in difficult category performance.