论文标题
广泛的共呈水位对象检测
Generalised Co-Salient Object Detection
论文作者
论文摘要
我们提出了一个新的设置，该设置通过允许的“嘈杂图像”的存在来放松传统的共同对象检测（COSOD）设置的假设，而“嘈杂的图像”不会显示共享的共同空位对象。我们称此新设置为广义的共同对象检测（GCOSOD）。我们提出了一种新型的基于随机抽样的广义COSOD训练（GCT）策略，以提炼对COSOD模型的图像间缺失的意识。它采用了多样的抽样自我监督学习（DS3L），除了提供的监督的共同空位标签外，还为嘈杂的图像引入了其他自我监督标签（无效，没有共同降低对象）。此外，GCT中固有的随机抽样过程可以生成高质量的不确定性映射，从而突出了实例级别的潜在假阳性预测。为了评估GCOSOD设置下COSOD模型的性能，我们提出了两个新的测试数据集，即可口可乐和可口可乐，其中一个共同的显着对象部分存在于前者中，并且在后者中完全不存在。广泛的实验表明，我们提出的方法可以显着改善COSOD模型的性能，从GCOSOD设置和模型校准度下的性能方面。
We propose a new setting that relaxes an assumption in the conventional Co-Salient Object Detection (CoSOD) setting by allowing the presence of "noisy images" which do not show the shared co-salient object. We call this new setting Generalised Co-Salient Object Detection (GCoSOD). We propose a novel random sampling based Generalised CoSOD Training (GCT) strategy to distill the awareness of inter-image absence of co-salient objects into CoSOD models. It employs a Diverse Sampling Self-Supervised Learning (DS3L) that, in addition to the provided supervised co-salient label, introduces additional self-supervised labels for noisy images (being null, that no co-salient object is present). Further, the random sampling process inherent in GCT enables the generation of a high-quality uncertainty map highlighting potential false-positive predictions at instance level. To evaluate the performance of CoSOD models under the GCoSOD setting, we propose two new testing datasets, namely CoCA-Common and CoCA-Zero, where a common salient object is partially present in the former and completely absent in the latter. Extensive experiments demonstrate that our proposed method significantly improves the performance of CoSOD models in terms of the performance under the GCoSOD setting as well as the model calibration degrees.
