  Camouflaged object detection (COD) aims to detect/segment camouflaged objects
embedded in the environment, which has attracted increasing attention over the
past decades. Although several COD methods have been developed, they still
suffer from unsatisfactory performance due to the intrinsic similarities
between the foreground objects and background surroundings. In this paper, we
propose a novel Feature Aggregation and Propagation Network (FAP-Net) for
camouflaged object detection. Specifically, we propose a Boundary Guidance
Module (BGM) to explicitly model the boundary characteristic, which can provide
boundary-enhanced features to boost the COD performance. To capture the scale
variations of the camouflaged objects, we propose a Multi-scale Feature
Aggregation Module (MFAM) to characterize the multi-scale information from each
layer and obtain the aggregated feature representations. Furthermore, we
propose a Cross-level Fusion and Propagation Module (CFPM). In the CFPM, the
feature fusion part can effectively integrate the features from adjacent layers
to exploit the cross-level correlations, and the feature propagation part can
transmit valuable context information from the encoder to the decoder network
via a gate unit. Finally, we formulate a unified and end-to-end trainable
framework where cross-level features can be effectively fused and propagated
for capturing rich context information. Extensive experiments on three
benchmark camouflaged datasets demonstrate that our FAP-Net outperforms other
state-of-the-art COD models. Moreover, our model can be extended to the polyp
segmentation task, and the comparison results further validate the
effectiveness of the proposed model in segmenting polyps. The source code and
results will be released at https://github.com/taozh2017/FAPNet.
