Training Robust Object Detectors From Noisy Category Labels and Imprecise Bounding Boxes

Weakly-Supervised Salient Object Detection With Saliency Bounding Boxes

“…On the other hand, annotating a large amount of common objects in the natural context is expensive and time-consuming. To reduce the annotation cost [47], dataset producers may rely on social media platforms or crowd-sourcing platforms. Nevertheless, the above strategies would lead to low-quality annotations.…”

“…Nevertheless, the above strategies would lead to low-quality annotations. Recent work [47] argues that the object detectors will suffer from the degenerated data. In addition, even large-scale datasets (e.g., MS-COCO) are dedicated annotated, box ambiguities [20] still exist.…”

“…Recently, learning object detectors with noisy data have gained a surge of interest, several approaches [1,5,25,47] have made attempted to tackle noisy annotations. These approaches often assume that the noise occurs both on category labels and bounding box annotations, and devise a disentangled architecture to learn object detectors.…”

“…A major line of research focuses on the classification task, and develops various techniques to deal with noisy labels, such as sample selection [18,21], label correction [32,37], and robust loss functions [16,50]. Recently, much effort [1,5,25,33,47] has been devoted to the object detection task. Simon et al [5] first investigate the impact of different types of label noise on object detection, and propose a per-object co-teaching method to alleviate the effect of noisy labels.…”

“…On the other hand, Li et al [25] propose a learning framework that alternately performs noise correction and model training to tackle noisy annotations, where the noisy annotations consist of noisy category labels and noisy bounding boxes. Xu et al [47] further introduce a meta-learning based approach to tackle noisy labels by leveraging a few clean samples.…”