Language-Guided Structure-Aware Network for Camouflaged Object Detection

Min Zhang

cs.CVMar 25, 2026

Language-Guided Structure-Aware Network for Camouflaged Object Detection

Authors: Min Zhang

Organizations: School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 401120, China

Abstract

Camouflaged Object Detection (COD) aims to segment objects that are highly integrated with the background in terms of color, texture, and structure, making it a highly challenging task in computer vision. Although existing methods introduce multi-scale fusion and attention mechanisms to alleviate the above issues, they generally lack the guidance of textual semantic priors, which limits the model's ability to focus on camouflaged regions in complex scenes. To address this issue, this paper proposes a Language-Guided Structure-Aware Network (LGSAN). Specifically, based on the visual backbone PVT-v2, we introduce CLIP to generate masks from text prompts and RGB images, thereby guiding the multi-scale features extracted by PVT-v2 to focus on potential target regions. On this foundation, we further design a Fourier Edge Enhancement Module (FEEM), which integrates multi-scale features with high-frequency information in the frequency domain to extract edge enhancement features. Furthermore, we propose a Structure-Aware Attention Module (SAAM) to effectively enhance the model's perception of object structures and boundaries. Finally, we introduce a Coarse-Guided Local Refinement Module (CGLRM) to enhance fine-grained reconstruction and boundary integrity of camouflaged object regions. Extensive experiments demonstrate that our method consistently achieves highly competitive performance across multiple COD datasets, validating its effectiveness and robustness.

Figures & tables

Figure 1: The architecture of LGSAN. The overall framework of the model consists of five key components: the PVT-v2 backbone, the CLIP backbone, the FEEM, the SAAM, and the CGRLM. Refer to Section 3 for details.

Figure 2: The architecture of the FEEM. The FEEM generates edge enhancement features through multi-scale fusion, edge enhancement, and high-frequency modeling in the frequency domain.

Figure 3: The SAAM introduces semantic information of camouflaged objects into a lightweight attention framework to highlight camouflaged regions, while incorporating edge enhancement features to emphasize boundary information, thereby enabling the model to focus on structural and boundary details of camouflaged objects at high resolution.

Figure 4: The CGLRM employs channel and spatial attention to obtain global guidance, performs local refinement through 2×2 spatial partitioning, thereby ensuring structural consistency and boundary integrity.

Figure 5: The heatmaps of LGSAN from $O_{4}$ to $O_{1}$ illustrate the progressive refinement process, while the heatmaps of $M_{1}$ and $O_{e}$ are also presented for comparison.

Method	Pub./Year	CAMO				COD10K				NC4k
Method	Pub./Year	$S_{\alpha}\uparrow$	$E_{\phi}\uparrow$	$F^{w}_{\beta}\uparrow$	$M\downarrow$	$S_{\alpha}\uparrow$	$E_{\phi}\uparrow$	$F^{w}_{\beta}\uparrow$	$M\downarrow$	$S_{\alpha}\uparrow$	$E_{\phi}\uparrow$	$F^{w}_{\beta}\uparrow$	$M\downarrow$
BGNet	IJCAI’22	0.812	0.870	0.749	0.073	0.831	0.901	0.722	0.033	0.851	0.907	0.788	0.044
ZoomNet	CVPR’22	0.820	0.892	0.752	0.066	0.838	0.911	0.729	0.029	0.853	0.912	0.784	0.059
EVP	CVPR’23	0.846	0.895	0.777	0.059	0.843	0.907	0.742	0.029
EAMNet	ICME’23	0.831	0.890	0.763	0.064	0.839	0.907	0.733	0.029	0.862	0.916	0.801	0.040
FSPNet	CVPR’23	0.856	0.899	0.799	0.050	0.851	0.895	0.735	0.026	0.879	0.915	0.816	0.035

Table 1: Quantitative comparison with state-of-the-art methods for COD on 3 benchmarks using 4 widely used evaluation metrics (

S_{\alpha},E_{\phi},F^{w}_{\beta},M

). “↑” / “↓” indicates that larger/smaller is better. The best results are in bold ,**** means the model was not tested on this dataset.

Figure 6: Compared with existing methods, the proposed approach achieves superior performance in terms of localization accuracy, structural integrity, and boundary details across multiple categories of camouflaged objects.

Model	Method	COD10K
Model	Method	$S_{\alpha}\uparrow$	$E_{\phi}\uparrow$	$F^{w}_{\beta}\uparrow$	$M\downarrow$
a	B	0.831	0.905	0.706	0.032
b	B + C	0.890	0.947	0.825	0.019
c	B + C + E	0.893	0.948	0.830	0.018
d	B + C + E + SC	0.894	0.950	0.833	0.018

Table 2: Ablation study of LGSAN. In the table, “B” denotes the baseline composed of PVT-v2-B3 and a simple CNN, “E” represents the FEEM module, “c” indicates the use of CLIP for generating the

M_{1}

mask, and “SC” refers to the SAAM and CGLRM.

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Text-Guided Image Segmentation Vision-Language Alignment Multimodal Fusion Camouflaged Object Detection

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Camouflaged object detection (COD) aims to segment objects that exhibit high visual similarity to their surroundings, which reduces foreground-background discriminability and weakens boundary evidence across appearance, texture, and structure. Such limitations motivate the use of instruction-conditioned semantics as top-down guidance for identifying which weak visual cues are relevant to the target. Recent segmentation systems built on large multimodal models (LMMs) demonstrate this possibility through instruction-conditioned target embeddings that guide mask decoding. However, in this language-to-mask paradigm, the generated target embedding conditions mainly the mask decoder, leaving the dense visual features that must preserve low-contrast boundaries and fine local structure without explicit guidance. We propose Language-Aligned Dense perception for COD (LAD-COD), a framework that aligns top-down semantic target guidance with bottom-up hierarchical visual features. Instead of fully adapting a large generic image encoder, LAD-COD learns a trainable hierarchical visual branch that captures camouflage-sensitive texture, boundary, and contextual information. To align these features with the target embedding, LAD-COD applies Language-Aligned Dual Visual Fusion (LADVF), which extends the embedding beyond sparse prompting to query patch-level language-aligned features and to gate their residual integration with the hierarchical features. This design allows semantic information to guide localization while preserving the fine structural details needed for camouflage segmentation. Experiments on CAMO, COD10K, and NC4K show that LAD-COD obtains the best reported value in all 12 dataset-metric comparisons.

Abstract

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