Generic object detection is a category-independent task that relies on
accurate modeling of objectness. Most relevant CNN-based models of objectness
utilize loss functions (e.g., binary cross entropy) that focus on the
single-response, i.e., the loss response of a single pixel. Inspired by the
human visual system, which first discerns the boundaries of ambiguous regions
(i.e., hard regions) before delving into the semantic meaning, we propose a
novel loss function, Spatial Coherence Loss (SCLoss), that uses the mutual
response between adjacent pixels to suppress or emphasize the single-response
of pixels. We demonstrate that the proposed SCLoss can gradually learn the hard
regions by detecting and emphasizing their boundaries. Through comprehensive
experiments, we demonstrate that replacing popular loss functions with SCLoss
can improve the performance of current state-of-the-art (SOTA) salient or
camouflaged object detection (SOD or COD) models. We also demonstrate that
combining SCLoss with other loss functions can further improve performance and
result in the SOTA outcomes for different applications. Finally, as a
demonstrative example of the potential uses for other related tasks, we show an
application of SCLoss for semantic segmentation.

通用物体检测是一项与类别无关的任务，它依赖于对象性的准确建模。我们提出了一种新的损失函数，即空间一致性损失（SCLoss），它利用相邻像素之间的相互响应来抑制或强调像素的单一响应，通过检测和强调边界逐渐学习困难区域。通过全面的实验证明，使用 SCLoss 替代流行的损失函数可以提高当前最先进的显著或伪装物体检测（SOD 或 COD）模型的性能。同时，我们还展示了将 SCLoss 与其他损失函数结合可以进一步提高性能并实现不同应用领域的最先进结果。最后，我们展示了将 SCLoss 应用于语义分割等其他相关任务的潜在用途的示例。