The recently proposed camouflaged object detection (COD) attempts to segment
objects that are visually blended into their surroundings, which is extremely
complex and difficult in real-world scenarios. Apart from high intrinsic
similarity between the camouflaged objects and their background, the objects
are usually diverse in scale, fuzzy in appearance, and even severely occluded.
To deal with these problems, we propose a mixed-scale triplet network,
\textbf{ZoomNet}, which mimics the behavior of humans when observing vague
images, i.e., zooming in and out. Specifically, our ZoomNet employs the zoom
strategy to learn the discriminative mixed-scale semantics by the designed
scale integration unit and hierarchical mixed-scale unit, which fully explores
imperceptible clues between the candidate objects and background surroundings.
Moreover, considering the uncertainty and ambiguity derived from
indistinguishable textures, we construct a simple yet effective regularization
constraint, uncertainty-aware loss, to promote the model to accurately produce
predictions with higher confidence in candidate regions. Without bells and
whistles, our proposed highly task-friendly model consistently surpasses the
existing 23 state-of-the-art methods on four public datasets. Besides, the
superior performance over the recent cutting-edge models on the SOD task also
verifies the effectiveness and generality of our model. The code will be
available at https://github.com/lartpang/ZoomNet.

本文提出了一种基于缩放网络的对象分割模型 ZoomNet，采用缩放策略、混合尺度单元和层级混合尺度单元学习圈定隐藏目标并能够适应模糊的背景干扰，用不确定性感知损失提高模型在候选区域中的预测准确度，大幅超越其他 23 种最先进的模型，在 SOD 任务上也取得了最优效果。