Assisted Refinement Network Based on Channel Information Interaction for Camouflaged and Salient Object Detection

TL;DR

This paper introduces a novel network architecture for camouflaged and salient object detection that enhances feature interaction and boundary modeling, achieving state-of-the-art results and demonstrating versatility across related tasks.

Contribution

The paper proposes the Channel Information Interaction Module and a collaborative decoding architecture with boundary and region extraction, advancing feature interaction and boundary accuracy in object detection.

Findings

Achieves state-of-the-art performance on four COD benchmarks.

Effectively transfers to salient object detection and other downstream tasks.

Demonstrates robustness and versatility across various applications.

Abstract

Camouflaged Object Detection (COD) stands as a significant challenge in computer vision, dedicated to identifying and segmenting objects visually highly integrated with their backgrounds. Current mainstream methods have made progress in cross-layer feature fusion, but two critical issues persist during the decoding stage. The first is insufficient cross-channel information interaction within the same-layer features, limiting feature expressiveness. The second is the inability to effectively co-model boundary and region information, making it difficult to accurately reconstruct complete regions and sharp boundaries of objects. To address the first issue, we propose the Channel Information Interaction Module (CIIM), which introduces a horizontal-vertical integration mechanism in the channel dimension. This module performs feature reorganization and interaction across channels to effectively capture complementary cross-channel information. To address the second issue, we construct a collaborative decoding architecture guided by prior knowledge. This architecture generates boundary priors and object localization maps through Boundary Extraction (BE) and Region Extraction (RE) modules, then employs hybrid attention to collaboratively calibrate decoded features, effectively overcoming semantic ambiguity and imprecise boundaries. Additionally, the Multi-scale Enhancement (MSE) module enriches contextual feature representations. Extensive experiments on four COD benchmark datasets validate the effectiveness and state-of-the-art performance of the proposed model. We further transferred our model to the Salient Object Detection (SOD) task and demonstrated its adaptability across downstream tasks, including polyp segmentation, transparent object detection, and industrial and road defect detection. Code and experimental results are publicly available at: https://github.com/akuan1234/ARNet-v2.