High-Resolution Iterative Feedback Network for Camouflaged Object Detection

Authors

  • Xiaobin Hu Tencent Youtu Lab
  • Shuo Wang ETH Zurich
  • Xuebin Qin Mohamed bin Zayed University of Artificial Intelligence
  • Hang Dai University of Glasgow
  • Wenqi Ren Sun Yat-Sen University
  • Donghao Luo Tencent Youtu Lab
  • Ying Tai Tencent Youtu Lab
  • Ling Shao Terminus Group

DOI:

https://doi.org/10.1609/aaai.v37i1.25167

Keywords:

CV: Scene Analysis & Understanding, CV: Object Detection & Categorization, CV: Segmentation

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. To design better feedback feature flow and avoid the feature corruption caused by recurrent path, an iterative feedback strategy 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. In addition, to address the data scarcity in camouflaged scenarios, we provide an application example to convert the salient objects to camouflaged objects, thereby generating more camouflaged training samples from the diverse salient object datasets. Code will be made publicly available.

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Published

2023-06-26

How to Cite

Hu, X., Wang, S., Qin, X., Dai, H., Ren, W., Luo, D., Tai, Y., & Shao, L. (2023). High-Resolution Iterative Feedback Network for Camouflaged Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 37(1), 881-889. https://doi.org/10.1609/aaai.v37i1.25167

Issue

Section

AAAI Technical Track on Computer Vision I