Conditional Polarization Guidance for Camouflaged Object Detection

QIfan Zhang; Hao Wang; Xiangrong Qin; Ruijie Li

cs.CVMar 31, 2026

Conditional Polarization Guidance for Camouflaged Object Detection

Authors: QIfan Zhang, Hao Wang, Xiangrong Qin, Ruijie Li

Organizations: Dalian Maritime University, Dalian, China · Dalian University of Technology, Dalian, China · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

Abstract

Camouflaged object detection (COD) aims to identify targets that are highly blended with their backgrounds. Recent works have shown that the optical characteristics of polarization cues play a significant role in improving camouflaged object detection. However, most existing polarization-based approaches depend on complex visual encoders and fusion mechanisms, leading to increased model complexity and computational overhead, while failing to fully explore how polarization can explicitly guide hierarchical RGB representation learning. To address these limitations, we propose CPGNet, an asymmetric RGB-polarization framework that introduces a conditional polarization guidance mechanism to explicitly regulate RGB feature learning for camouflaged object detection. Specifically, we design a lightweight polarization interaction module that jointly models these complementary cues and generates reliable polarization guidance in a unified manner. Unlike conventional feature fusion strategies, the proposed conditional guidance mechanism dynamically modulates RGB features using polarization priors, enabling the network to focus on subtle discrepancies between camouflaged objects and their backgrounds. Furthermore, we introduce a polarization edge-guided frequency refinement strategy that enhances high-frequency components under polarization constraints, effectively breaking camouflage patterns. Finally, we develop an iterative feedback decoder to perform coarse-to-fine feature calibration and progressively refine camouflage prediction. Extensive experiments on polarization datasets across multiple tasks, along with evaluations on non-polarization datasets, demonstrate that CPGNet consistently outperforms state-of-the-art methods.

Figures & tables

Figure 1. Comparison of RGB–polarization integration paradigms. (a) Attention fusion enhances RGB features using polarization-aware attention. (b) Mid-level fusion directly merges RGB and polarization features. (c) Our method treats polarization as conditional guidance for RGB representation learning, rather than explicit feature fusion.

Panel of Figure 2 — Figure 2. (a) Overview of the proposed CPGNet. (b) Polarization Integration Module (PIM). (c) Polarization Guidance Enhancement (PGE). (d) Edge-guided Frequency Module (EFM). (e) Feature Refinement (FR). CPGNet progressively injects polarization guidance into hierarchical RGB features and refines predictions in a coarse-to-fine manner.

Figure 3. Qualitative comparison with SOTA methods on PCOD-1200. CPGNet produces more complete predictions and cleaner boundaries in challenging camouflage scenes.

Methods	Pub.’Year	$S_{\alpha}\uparrow$	$E_{\phi}\uparrow$	$M\downarrow$	$F_{\beta}^{w}\uparrow$
RGB-only Methods
PFNet ( , 2021 )	CVPR’21	0.876	0.946	0.013	0.788
SINet-V2 ( Fan et al., 2020a )	TPAMI’22	0.882	0.941	0.013	-
HQSAM* ( Ke et al., 2023 )	NeurIPS’23	0.936	0.973	0.008	0.909
FEDER ( He et al., 2023 )	CVPR’23	0.892	0.946	0.011	0.843
FSEL ( Sun et al., 2024 )	ECCV’24	0.934	0.978	0.006	-

Table 1. Quantitative comparisons with SOTAs on PCOD-1200. The best results are highlighted in bold . * represents the SAM-based methods.

Methods	$OA$ (%) $\uparrow$	$M$ $\downarrow$	$IoU$ (%) $\uparrow$	$BER$ (%) $\downarrow$	$F_{\beta}$ (%) $\uparrow$
MirrorNet 19 ( Yang et al., 2019 )	89.44	0.121	79.22	11.30	87.02
SINetv2 22 ( Fan et al., 2020a )	88.78	0.117	79.85	11.05	86.73
PGSNet 22 ( Mei et al., 2022 )	90.98	0.091	81.8	9.63	89.38
PolarNet 23 ( Wang et al., 2023b )	91.15	0.099	83.11	9.66	89.84
IPNet 24 ( , 2024 )	91.08	0.098	81.83	8.97	90.15
RISNet 24 ( Wang et al., 2024 )	84.54	0.158	73.70	15.21	82.10

Table 2. Quantitative comparisons with multimodal SOTAs on the RGBP-Glass dataset. The best results are highlighted in bold .

Figure 4. Qualitative comparison of our CPGNet against SOTA approaches on the RGBP-Glass dataset.

Figure 5. Qualitative comparison of our CPGNet against SOTA approaches on the RGB-Depth datasets.

Methods	Params(M)↓	CAMO				COD10K				NC4K				CHAMELEON
		$S_{\alpha}\uparrow$	$F_{\beta}^{w}\uparrow$	$E_{\phi}\uparrow$	$M\downarrow$	$S_{\alpha}\uparrow$	$F_{\beta}^{w}\uparrow$	$E_{\phi}\uparrow$	$M\downarrow$	$S_{\alpha}\uparrow$	$F_{\beta}^{w}\uparrow$	$E_{\phi}\uparrow$	$M\downarrow$	$S_{\alpha}\uparrow$	$F_{\beta}^{w}\uparrow$	$E_{\phi}\uparrow$	$M\downarrow$
PFNet ( , 2021 )	48.55M	0.782	0.695	0.845	0.085	0.800	0.660	0.880	0.040	0.829	0.745	0.891	0.053	0.882	0.810	0.927	0.053
SPSN ( Lee et al., )	34.04M	0.773	0.782	0.829	0.084	0.789	0.727	0.854	0.042	0.852	0.852	0.908	0.043	-	-	-	-
PopNet ( Wu et al., 2023 )	198.9M	0.808	0.744	0.859	0.077	0.851	0.757	0.910	0.028	0.861	0.808	0.910	0.042	0.910	0.893	-	0.022
PlantCamo ( Yang et al., 2024 )	27.66 M	0.819	0.757	0.875	0.076	0.856	0.767	0.911	0.029	0.853	0.789	0.899	0.047	0.899	0.853	0.941	0.030
UTNet ( Sun et al., 2025 )	10.02M	0.677	0.518	0.733	0.123	0.699	0.485	0.773	0.068	0.743	0.603	0.798	0.086	0.818	0.709	0.885	0.054

Table 3. Comparison of SOTA methods on multiple RGB-Depth datasets in terms of

S_{\alpha}

F_{\beta}^{w}

E_{\phi}

, and

M

. The best results are highlighted in bold .

#	Networks	$S_{\alpha}\uparrow$	$E_{\phi}\uparrow$	$M\downarrow$	$F_{\beta}^{w}\uparrow$
A	RGB only	0.942	0.976	0.006	0.909
B	RGB+AoLP	0.945	0.979	0.006	0.915
C	RGB+DoLP	0.947	0.980	0.005	0.917
D	w/o PGE	0.944	0.979	0.006	0.910
E	PGE w/o $\alpha,\beta$	0.947	0.980	0.006	0.918
F	PGE w/o Enhance	0.943	0.976	0.005	0.910

Table 4. Ablation study on the PCOD-1200 dataset.

Figure 6. Qualitative ablation results on PCOD-1200. Each component improves prediction completeness and boundary quality, and the full model yields the best visual results.

Figure 7. Failure cases caused by degraded polarization quality. When DoLP/AoLP measurements are weak, noisy, or unstable, the derived guidance may become unreliable, leading to inaccurate localization and incomplete segmentation.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 8. Additional qualitative comparisons on RGBP-Glass dataset.

Figure 9. Additional qualitative comparisons on PCOD-1200.

Figure 10. Additional qualitative comparisons on RGB-Depth datasets.

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