Spatial Coherence Loss: All Objects Matter in Salient and Camouflaged Object Detection

Understand

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.