  Spotting camouflaged objects that are visually assimilated into the
background is tricky for both object detection algorithms and humans who are
usually confused or cheated by the perfectly intrinsic similarities between the
foreground objects and the background surroundings. To tackle this challenge,
we aim to extract the high-resolution texture details to avoid the detail
degradation that causes blurred vision in edges and boundaries. We introduce a
novel HitNet to refine the low-resolution representations by high-resolution
features in an iterative feedback manner, essentially a global loop-based
connection among the multi-scale resolutions. In addition, an iterative
feedback loss is proposed to impose more constraints on each feedback
connection. Extensive experiments on four challenging datasets demonstrate that
our \ourmodel~breaks the performance bottleneck and achieves significant
improvements compared with 29 state-of-the-art methods. To address the data
scarcity in camouflaged scenarios, we provide an application example by
employing cross-domain learning to extract the features that can reflect the
camouflaged object properties and embed the features into salient objects,
thereby generating more camouflaged training samples from the diverse salient
object datasets The code will be available at
https://github.com/HUuxiaobin/HitNet.
