High-resolution Iterative Feedback Network for Camouflaged Object Detection

Authors

Xiaobin Hu, Tencent Youtu Lab, Shanghai, China
Deng-Ping Fan, Cvl, Eth Zurich, Zurich, Switzerland
Xuebin Qin, Mohamed Bin Zayed University of Artificial Intelligence
Hang Dai, Mohamed bin Zayed University of Artificial IntelligenceFollow
Wenqi Ren, Sun Yat-sen University, Shenzhen, China
Ying Tai, Tencent Youtu Lab, Shanghai, China
Chengjie Wang, Tencent Youtu Lab, Shanghai, China
Ling Shao, Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

arXiv

Abstract

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.

DOI

doi.org/10.48550/arXiv.2203.11624

Publication Date

3-22-2022

Keywords

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)

Comments

IR Deposit conditions: non-described

Preprint available on arXiv

Recommended Citation

X. Hu et al, "High-resolution Iterative Feedback Network for Camouflaged Object Detection", arXiv, Mar 2022, doi: 10.48550/arXiv.2203.11624