论文标题
伪装对象检测的高分辨率迭代反馈网络
High-resolution Iterative Feedback Network for Camouflaged Object Detection
论文作者
论文摘要
对于对象检测算法和人类，发现伪装成背景中的伪装物体都是棘手的，这些物体通常被前景对象和背景周围环境之间完全固有的相似性所困惑或欺骗。为了应对这一挑战，我们旨在提取高分辨率的纹理细节，以避免细节退化，从而导致边缘和边界的视力模糊。我们介绍了一种新颖的HITNET，以迭代反馈方式通过高分辨率特征来完善低分辨率表示形式，从本质上讲，在多尺度的分辨率之间是基于全球循环的连接。此外，提出了迭代反馈损失，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广泛实验表明，与29种最先进的方法相比，我们的\ ouremodel〜打破了性能瓶颈并取得了重大改进。 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.
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.
