The task of Camouflaged Object Detection (COD) aims to accurately segment
camouflaged objects that integrated into the environment, which is more
challenging than ordinary detection as the texture between the target and
background is visually indistinguishable. In this paper, we proposed a novel
Feature Grafting and Distractor Aware network (FDNet) to handle the COD task.
Specifically, we use CNN and Transformer to encode multi-scale images in
parallel. In order to better explore the advantages of the two encoders, we
design a cross-attention-based Feature Grafting Module to graft features
extracted from Transformer branch into CNN branch, after which the features are
aggregated in the Feature Fusion Module. A Distractor Aware Module is designed
to explicitly model the two possible distractors in the COD task to refine the
coarse camouflage map. We also proposed the largest artificial camouflaged
object dataset which contains 2000 images with annotations, named ACOD2K. We
conducted extensive experiments on four widely used benchmark datasets and the
ACOD2K dataset. The results show that our method significantly outperforms
other state-of-the-art methods. The code and the ACOD2K will be available at
this https URL

本研究提出了一种新的特征融合和多干扰物注视的网络，用于解决融入环境中的伪装目标检测问题，并通过自行构建的 ACOD2K 数据集验证其性能。

特征引入与干扰物感知的伪装物体检测

Camouflaged Object Detection with Feature Grafting and Distractor Aware

Although supervised deep stereo matching networks have made impressive
achievements, the poor generalization ability caused by the domain gap prevents
them from being applied to real-life scenarios. In this paper, we propose to
leverage the feature of a model trained on large-scale datasets to deal with
the domain shift since it has seen various styles of images. With the cosine
similarity based cost volume as a bridge, the feature will be grafted to an
ordinary cost aggregation module. Despite the broad-spectrum representation,
such a low-level feature contains much general information which is not aimed
at stereo matching. To recover more task-specific information, the grafted
feature is further input into a shallow network to be transformed before
calculating the cost. Extensive experiments show that the model generalization
ability can be improved significantly with this broad-spectrum and
task-oriented feature. Specifically, based on two well-known architectures
PSMNet and GANet, our methods are superior to other robust algorithms when
transferring from SceneFlow to KITTI 2015, KITTI 2012, and Middlebury. Code is
available at this https URL

利用大规模数据集训练的模型特征，采用余弦相似度为基础的代价体积作为桥梁，将特征嫁接到一个普通的代价聚合模块，以此来解决领域转化带来的一般化能力不足。通过将嫁接特征输入到一个浅层网络进行变换计算代价，从而恢复更多的任务 - specific 信息，大量实验表明，这种宽光谱和任务定向的特征可以显著提高模型的泛化能力。