  Automatically detecting/segmenting object(s) that blend in with their
surroundings is difficult for current models. A major challenge is that the
intrinsic similarities between such foreground objects and background
surroundings make the features extracted by deep model indistinguishable. To
overcome this challenge, an ideal model should be able to seek valuable, extra
clues from the given scene and incorporate them into a joint learning framework
for representation co-enhancement. With this inspiration, we design a novel
Mutual Graph Learning (MGL) model, which generalizes the idea of conventional
mutual learning from regular grids to the graph domain. Specifically, MGL
decouples an image into two task-specific feature maps -- one for roughly
locating the target and the other for accurately capturing its boundary details
-- and fully exploits the mutual benefits by recurrently reasoning their
high-order relations through graphs. Importantly, in contrast to most mutual
learning approaches that use a shared function to model all between-task
interactions, MGL is equipped with typed functions for handling different
complementary relations to maximize information interactions. Experiments on
challenging datasets, including CHAMELEON, CAMO and COD10K, demonstrate the
effectiveness of our MGL with superior performance to existing state-of-the-art
methods.
