  Recent research about camouflaged object detection (COD) aims to segment
highly concealed objects hidden in complex surroundings. The tiny, fuzzy
camouflaged objects result in visually indistinguishable properties. However,
current single-view COD detectors are sensitive to background distractors.
Therefore, blurred boundaries and variable shapes of the camouflaged objects
are challenging to be fully captured with a single-view detector. To overcome
these obstacles, we propose a behavior-inspired framework, called Multi-view
Feature Fusion Network (MFFN), which mimics the human behaviors of finding
indistinct objects in images, i.e., observing from multiple angles, distances,
perspectives. Specifically, the key idea behind it is to generate multiple ways
of observation (multi-view) by data augmentation and apply them as inputs. MFFN
captures critical boundary and semantic information by comparing and fusing
extracted multi-view features. In addition, our MFFN exploits the dependence
and interaction between views and channels. Specifically, our methods leverage
the complementary information between different views through a two-stage
attention module called Co-attention of Multi-view (CAMV). And we design a
local-overall module called Channel Fusion Unit (CFU) to explore the
channel-wise contextual clues of diverse feature maps in an iterative manner.
The experiment results show that our method performs favorably against existing
state-of-the-art methods via training with the same data. The code will be
available at https://github.com/dwardzheng/MFFN_COD.

Computer Vision and Pattern Recognition