Nested Unfolding Network for Real-World Concealed Object Segmentation

• URL: http://arxiv.org/abs/2511.18164v1
• Date: Sat, 22 Nov 2025 19:25:48 GMT
• Title: Nested Unfolding Network for Real-World Concealed Object Segmentation
• Abstract summary: We propose a nested unfolding network (NUN) for concealed object segmentation.<n>NUN embeds a degradation-resistant unfolding network (DeRUN) within each stage of a segmentation-oriented unfolding network (SODUN)<n>In experiments, NUN achieves a leading place on both clean and degraded benchmarks.
• Score: 25.42332359957824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep unfolding networks (DUNs) have recently advanced concealed object segmentation (COS) by modeling segmentation as iterative foreground-background separation. However, existing DUN-based methods (RUN) inherently couple background estimation with image restoration, leading to conflicting objectives and requiring pre-defined degradation types, which are unrealistic in real-world scenarios. To address this, we propose the nested unfolding network (NUN), a unified framework for real-world COS. NUN adopts a DUN-in-DUN design, embedding a degradation-resistant unfolding network (DeRUN) within each stage of a segmentation-oriented unfolding network (SODUN). This design decouples restoration from segmentation while allowing mutual refinement. Guided by a vision-language model (VLM), DeRUN dynamically infers degradation semantics and restores high-quality images without explicit priors, whereas SODUN performs reversible estimation to refine foreground and background. Leveraging the multi-stage nature of unfolding, NUN employs image-quality assessment to select the best DeRUN outputs for subsequent stages, naturally introducing a self-consistency loss that enhances robustness. Extensive experiments show that NUN achieves a leading place on both clean and degraded benchmarks. Code will be released.

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