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.

翻译：从视觉上融入背景的伪装目标检测对目标检测算法和人类而言均具有挑战性，因为前景目标与背景环境之间完美的内在相似性常常使人困惑或受骗。为应对这一挑战，我们旨在提取高分辨率纹理细节，以避免导致边缘和边界视觉模糊的细节退化问题。我们提出了一种新颖的HitNet，通过高分辨率特征以迭代反馈方式优化低分辨率表示，本质上是一种基于多尺度分辨率间的全局循环连接。此外，我们引入了迭代反馈损失函数，以对每个反馈连接施加更多约束。在四个具有挑战性的数据集上进行的大量实验表明，我们的模型打破了性能瓶颈，与29种最先进方法相比取得了显著改进。针对伪装场景中的数据稀缺问题，我们提供了一个应用示例：通过跨领域学习提取能够反映伪装目标属性的特征，并将这些特征嵌入显著目标中，从而从多样化的显著目标数据集中生成更多伪装训练样本。代码将发布于https://github.com/HUuxiaobin/HitNet。