Bi-RRNet: Bi-level recurrent refinement network for camouflaged object detection. (July 2023)

• Record Type: Journal Article
• Authors: Liu, Yan; Zhang, Kaihua; Zhao, Yaqian; Chen, Hu; Liu, Qingshan
• Is Part Of: Pattern recognition. Volume 139(2023)
• Publication Date: 2023-07
• Subjects: Camouflaged object detection -- Convolutional neural networks -- Recurrent refinement network -- Dense prediction; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2023.109514
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose a novel and lightweight Bi-RRNet for COD that includes an LRRN and a U-RRN. Both L-RRN and U-RRN iteratively refine the multilevel context features to obtain a highly discriminative high-resolution semantic feature representation for precise dense prediction. We design two novel modules for intra- and inter-class feature enhancement including the MSPM and the RCEM. The MSPM uses global scene context signal to adjust the multi-scale features for each layer to mitigate the significant object appearance variations, while the RCEM is to highlight the camouflaged object region to enlarge the inter-class difference between the camouflaged objects and their surroundings. Extensive evaluations on four challenging benchmark datasets demonstrate that our Bi-RRNet achieves superior performance against 15 state of-the-art methods in terms of both accuracy and model parameters. Besides, our Bi-RRNet is lightweight with only 14.95M model parameters that are only half of the State-Of-The-Art (SOTA) BSA-Net [26]. 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.