Camouflaged Object Detection via Neural Architecture Search

Abstract

The core challenge in camouflaged object detection (COD) is identifying objects that blend seamlessly with their surroundings. Existing methods emulate the strategies biological organisms break camouflage by manually constructing modules with expert knowledge from existing segmentation tasks, making it difficult to accurately understand complex and unique camouflage semantics. We are the first to apply neural architecture search (NAS) to COD, introducing an automatic localization and refinement network called ALRNet. It explores a large search space to discover more effective camouflage-specific modules. Specifically, we propose a search-based automatic receptive field block (ARFB) to adaptively excavate hierarchical discriminative cues and decouple features in a multi-branch architecture. Moreover, we introduce an edge-assisted explicit and implicit refinement (EEIR) module, combining explicit priors with implicit search to create a dual-task structure for edge and segmentation knowledge interaction. Experiments on four benchmarks demonstrate that ALRNet outperforms 15 state-of-the-art methods. Codes are available at https://github.com/BoydeLi/ALRNet.