Spatial Coherence Loss for Salient and Camouflaged Object Detection and
  Beyond
Ziyun Yang Kevin Choy Sina Farsiu
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.