High-resolution Iterative Feedback Network for Camouflaged Object Detection

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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 HitNet 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 the 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. Copyright © 2022, The Authors. All rights reserved.



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Iterative methods, Machine learning, Object recognition, Textures, Camouflaged object, Feedback networks, Foreground objects, High resolution, High-resolution textures, Iterative feedback manner, Lower resolution, Object detection algorithms, Objects detection, Salient objects, Object detection, Computer Vision and Pattern Recognition (cs.CV)


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Preprint available on arXiv