Spatial Coherence Loss: All Objects Matter in Salient and Camouflaged Object Detection
This work addresses the challenge of accurately detecting objects in complex visual scenes, particularly for applications like salient and camouflaged object detection, by introducing an incremental enhancement to loss functions that leverages object-level ambiguity learning.
The paper tackles the problem of improving object detection in salient and camouflaged scenarios by proposing a novel loss function, Spatial Coherence Loss (SCLoss), which incorporates mutual responses between adjacent pixels to better learn ambiguous regions, resulting in state-of-the-art performance improvements when integrated with existing models.
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.