research-article

PreyNet: Preying on Camouflaged Objects

Authors:

Huchuan LuAuthors Info & Claims

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

Pages 5323 - 5332

https://doi.org/10.1145/3503161.3548178

Published: 10 October 2022 Publication History

Abstract

Species often adopt various camouflage strategies to be seamlessly blended into the surroundings for self-protection. To figure out the concealment, predators have evolved excellent hunting skills. Exploring the intrinsic mechanisms of the predation behavior can offer more insightful glimpse into the task of camouflaged object detection (COD). In this work, we strive to seek answers for accurate COD and propose a PreyNet, which mimics the two processes of predation, namely, initial detection (sensory mechanism) and predator learning (cognitive mechanism). To exploit the sensory process, a bidirectional bridging interaction module (BBIM) is designed for selecting and aggregating initial features in an attentive manner. The predator learning process is formulated as a policy-and-calibration paradigm, with the goal of deciding on uncertain regions and encouraging targeted feature calibration. Besides, we obtain adaptive weight for multi-layer supervision during training via computing on the uncertainty estimation. Extensive experiments demonstrate that our model produces state-of-the-art results on several benchmarks. We further verify the scalability of the predator learning paradigm through applications on top-ranking salient object detection models. Our code is publicly available at \urlhttps://github.com/OIPLab-DUT/PreyNet.

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Cited By

Li GWang JWei JXu Z(2025)IRFNet: Cognitive-Inspired Iterative Refinement Fusion Network for Camouflaged Object DetectionSensors10.3390/s2505155525:5(1555)Online publication date: 3-Mar-2025
https://doi.org/10.3390/s25051555
He SYin CLi X(2025)Cross-Layer Semantic Guidance Network for Camouflaged Object DetectionElectronics10.3390/electronics1404077914:4(779)Online publication date: 17-Feb-2025
https://doi.org/10.3390/electronics14040779
Sun KChen ZLin XSun XLiu HJi R(2025)Conditional Diffusion Models for Camouflaged and Salient Object DetectionIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2025.352746947:4(2833-2848)Online publication date: Apr-2025
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Index Terms

PreyNet: Preying on Camouflaged Objects
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections

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cover image ACM Conferences

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

October 2022

7537 pages

ISBN:9781450392037

DOI:10.1145/3503161

General Chairs:
João Magalhães
NOVA University of Lisbon, Portugal
,
Alberto del Bimbo
University of Florence, Italy
,
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Nicu Sebe
University of Trento, Italy
,
Program Chairs:
Xavier Alameda-Pineda
Inria, Grenoble, France
,
Qin Jin
Renmin University of China, China
,
Vincent Oria
New Jersey Institute of Technology, USA
,
Laura Toni
University College London, UK

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Published: 10 October 2022

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the Natural Science Foundation of Liaoning Province
the Central Government Guided Local Science and Technology Development Funds of Liaoning Province
the National Natural Science Foundation of China

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MM '22

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MM '22: The 30th ACM International Conference on Multimedia

October 10 - 14, 2022

Lisboa, Portugal

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Li GWang JWei JXu Z(2025)IRFNet: Cognitive-Inspired Iterative Refinement Fusion Network for Camouflaged Object DetectionSensors10.3390/s2505155525:5(1555)Online publication date: 3-Mar-2025
https://doi.org/10.3390/s25051555
He SYin CLi X(2025)Cross-Layer Semantic Guidance Network for Camouflaged Object DetectionElectronics10.3390/electronics1404077914:4(779)Online publication date: 17-Feb-2025
https://doi.org/10.3390/electronics14040779
Sun KChen ZLin XSun XLiu HJi R(2025)Conditional Diffusion Models for Camouflaged and Salient Object DetectionIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2025.352746947:4(2833-2848)Online publication date: Apr-2025
https://doi.org/10.1109/TPAMI.2025.3527469
Yue GWu SZhou TLi GDu JLuo YJiang Q(2025)Progressive Region-to-Boundary Exploration Network for Camouflaged Object DetectionIEEE Transactions on Multimedia10.1109/TMM.2024.352176127(236-248)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TMM.2024.3521761
Li XLong SYang JLei JLi SZhang JCohen L(2025)DPSNet: A Detail Perception Synergistic Network for Camouflaged Object DetectionIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.349718174(1-14)Online publication date: 2025
https://doi.org/10.1109/TIM.2024.3497181
Zhang XZhou JYan LZhong SZou X(2025)Hunt Camouflaged Objects via Revealing Mutation RegionsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2025.353070320(1836-1851)Online publication date: 2025
https://doi.org/10.1109/TIFS.2025.3530703
Wu FYin JLi XWu JJin DYang J(2025)CoNet: A Consistency-Oriented Network for Camouflaged Object SegmentationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.346246535:1(287-299)Online publication date: Jan-2025
https://doi.org/10.1109/TCSVT.2024.3462465
Li MZhao YZhang FGui GLuo BYang CGui WChang K(2025)CSFIN: A lightweight network for camouflaged object detection via cross-stage feature interactionExpert Systems with Applications10.1016/j.eswa.2025.126451269(126451)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2025.126451
Ren JZhang QKang BZhong YHe MGe YBi H(2025)Semantic-spatial guided context propagation network for camouflaged object detectionApplied Intelligence10.1007/s10489-025-06264-055:5Online publication date: 20-Jan-2025
https://doi.org/10.1007/s10489-025-06264-0
Zhang MShen CDeng YWang L(2025)Camouflaged object detection via boundary refinementMultimedia Systems10.1007/s00530-024-01662-931:1Online publication date: 8-Jan-2025
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