Mutual Graph Learning for Camouflaged Object Detection
- URL: http://arxiv.org/abs/2104.02613v1
- Date: Sat, 3 Apr 2021 10:14:39 GMT
- Title: Mutual Graph Learning for Camouflaged Object Detection
- Authors: Qiang Zhai, Xin Li, Fan Yang, Chenglizhao Chen, Hong Cheng, Deng-Ping
Fan
- Abstract summary: A major challenge is that intrinsic similarities between foreground objects and background surroundings make the features extracted by deep model indistinguishable.
We design a novel Mutual Graph Learning model, which generalizes the idea of conventional mutual learning from regular grids to the graph domain.
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.
- Score: 31.422775969808434
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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