When CNN meet with ViT: decision-level feature fusion for camouflaged object detection

Despite the significant advancements in camouflaged object detection achieved by convolutional neural network (CNN) methods and vision transformer (ViT) methods, both have limitations. CNN-based methods fail to explore long-range dependencies due to their limited receptive fields, while ViT-based methods lose detailed information due to large-span aggregation. To address these issues, we introduce a novel model, the double-extraction and triple-fusion network (DTNet), which leverages the global context modeling capabilities of ViT-based encoders and the detail capture capabilities of CNN-based encoders through decision-level feature fusion to make up the respective shortcomings for more complete segmentation of camouflaged objects. Specifically, it incorporates a boundary guidance module, designed to aggregate high-level and low-level boundary information through multi-scale feature decoding, thereby guiding the local detail representation of the transformer. It also includes a global context aggregation module, which shrinks the information of adjacent channels from top to bottom and aggregates information of high-level and low-level scales from bottom to top for feature decoding. It also contains a multi-feature fusion module to fuse global context features and local detail features. This module employs the attention mechanism in different channels to assign varying weights to long-range and short-range information. Through extensive experimentation, it has proven that the DTNet significantly surpasses 20 recently state-of-the-art methods in terms of performance. The related code and datasets will be posted at https://github.com/KungFuProgrammerle/DTNet.