A Simple yet Effective Network based on Vision Transformer for
  Camouflaged Object and Salient Object Detection
Chao Hao Zitong Yu Xin Liu Jun Xu Huanjing Yue Jingyu Yang
Camouflaged object detection (COD) and salient object detection (SOD) are two
distinct yet closely-related computer vision tasks widely studied during the
past decades. Though sharing the same purpose of segmenting an image into
binary foreground and background regions, their distinction lies in the fact
that COD focuses on concealed objects hidden in the image, while SOD
concentrates on the most prominent objects in the image. Previous works
achieved good performance by stacking various hand-designed modules and
multi-scale features. However, these carefully-designed complex networks often
performed well on one task but not on another. In this work, we propose a
simple yet effective network (SENet) based on vision Transformer (ViT), by
employing a simple design of an asymmetric ViT-based encoder-decoder structure,
we yield competitive results on both tasks, exhibiting greater versatility than
meticulously crafted ones. Furthermore, to enhance the Transformer's ability to
model local information, which is important for pixel-level binary segmentation
tasks, we propose a local information capture module (LICM). We also propose a
dynamic weighted loss (DW loss) based on Binary Cross-Entropy (BCE) and
Intersection over Union (IoU) loss, which guides the network to pay more
attention to those smaller and more difficult-to-find target objects according
to their size. Moreover, we explore the issue of joint training of SOD and COD,
and propose a preliminary solution to the conflict in joint training, further
improving the performance of SOD. Extensive experiments on multiple benchmark
datasets demonstrate the effectiveness of our method. The code is available at
https://github.com/linuxsino/SENet.