Multi-Feature Fusion for Camouflaged Object Segmentation.

In the field of camouflaged object segmentation, how to extract high-resolution semantic features from a depth model is the key to constructing a target segmentation model. In order to better solve this problem, a new camouflage target segmentation method based on multi-level feature fusion is proposed. A multi-stage gate control module is introduced to selectively fuse the multi-stage middle layer features of Res2Net-50, which can effectively filter the interference information of each level feature map during the feature encoding process. And in decoding, the self-interaction residual module has been used to drive the cross-fusion of encoding features of different scales, which guarantees the obtaining of more accurate target representation information. In addition, this paper combines cross entropy loss and Dice loss as a joint loss function to help the model segment the camouflaged target more accurately. Experimental results show that the proposed model performs better than the other eight typical models in the complex background camouflage data set and three common natural camouflage datasets. [ABSTRACT FROM AUTHOR]