Bi-RRNet: Bi-level recurrent refinement network for camouflaged object detection

Abstract

In this paper, we present a lightweight Bi-level Recurrent Refinement Network (Bi-RRNet) for Camouflaged Object Detection (COD) that consists of a Lower-level RRNet (L-RRN) and an Up-level RRNet (U-RRN) to progressively refine the multi-level context features for precise dense prediction. In particular, the L-RRN recursively refines the deeper layer high-level semantic features with the high-resolution low-level features from the earlier layers in a top-down manner, and the U-RRN progressively polishes the refined features from the L-RRN in a recurrent manner, producing the high-resolution semantic features that are essential to accurate COD. Moreover, we develop a Multi-scale Scene Perception Module (MSPM) that, in order to deal with target appearance variation, first compresses the global scene context information at each layer into a learnable weight vector and then modulates the multi-scale context features produced by a filter bank with various local receptive fields using the learned weights. Meanwhile, we design a Region-Consistency Enhancement Module (RCEM) that makes use of high-level semantic features to direct filtering out the cluttered information in the lower-layer features. This module can highlight the regions of camouflaged objects, maximizing the inter-class contrast between the objects and their surroundings. Extensive experiments on four challenging benchmark datasets, including CHAMELEON, CAMO, COD10K, and NC4K, show that our Bi-RRNet outperforms a variety of state-of-the-art methods in terms of accuracy and model parameters. Our Bi-RRNet, in particular, is lightweight, with 14.95M parameters that are only half the size of the state-of-the-art BSA-Net.