  Generic object detection is a category-independent task that relies on
accurate modeling of objectness. We show that for accurate semantic analysis,
the network needs to learn all object-level predictions that appear at any
stage of learning, including the pre-defined ground truth (GT) objects and the
ambiguous decoy objects that the network misidentifies as foreground. Yet, most
relevant models focused mainly on improving the learning of the GT objects. A
few methods that consider decoy objects utilize loss functions that only focus
on the single-response, i.e., the loss response of a single ambiguous pixel,
and thus do not benefit from the wealth of information that an object-level
ambiguity learning design can provide. Inspired by the human visual system,
which first discerns the boundaries of ambiguous regions before delving into
the semantic meaning, we propose a novel loss function, Spatial Coherence Loss
(SCLoss), that incorporates the mutual response between adjacent pixels into
the widely-used single-response loss functions. We demonstrate that the
proposed SCLoss can gradually learn the ambiguous regions by detecting and
emphasizing their boundaries in a self-adaptive manner. 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 SOTA outcomes for different applications.
