HVPNet: A Bio-Inspired Network for General Salient and Camouflaged Object Detection

2026-06-30

AI Summary

Existing multimodal salient and camouflaged object detection methods suffer from structural complexity and large parameter counts, making it challenging to balance accuracy and efficiency. Inspired by the human visual system, this work proposes a lightweight, unified architecture that integrates a Retinal Integration Module (RIM) for hierarchical, multi-stage cross-modal feature fusion and a Cortical Decoder (CD) that mimics visual cortical mechanisms for layered decoding. This approach establishes a biologically inspired, simplified modeling paradigm capable of supporting diverse modalities and tasks within a single framework. Evaluated across four modalities, seven tasks, and 22 datasets, the model achieves an excellent trade-off between accuracy and efficiency with a compact structure, demonstrating strong generalization capability.

Abstract

In recent years, most research on multimodal salient object detection (SOD) and camouflaged object detection (COD) typically aims to improve performance through complex cross-modal feature fusion and decoding structures. However, this approach leads to an excessively large model parameter scale and often fails to deliver satisfactory detection performance due to structural redundancy. In contrast, the human visual process is able to efficiently perform salient and camouflaged object identification without such complex structures. This contrast raises an important question: Can we draw conceptual inspiration from the human visual process to achieve a simpler modeling strategy, and still realize accurate and efficient object detection? To answer this question, we propose HVPNet, a simple yet general bio-inspired computational architecture. Drawing on the multi-layered information integration of the retina as a conceptual metaphor, we designed a Retinal Integration Module (RIM), which effectively integrates multimodal features through a level-specific multi-stage integration strategy. To fully exploit these features, we further design a cortical decoder (CD) that breaks down the decoding process into low- and high-level visual stages, abstracting the hierarchical processing in the human visual cortex. Benefiting from these designs, HVPNet can readily extend to seven tasks across four modalities. Without bells and whistles, it establishes an excellent accuracy-efficiency trade-off across 22 datasets spanning these seven tasks. Our code is available at https://github.com/jiaweiXu1029/HVPNet.

Problem

• salient object detection
• camouflaged object detection
• multimodal fusion
• model efficiency
• structural redundancy

Innovation

• bio-inspired
• retinal integration
• cortical decoding
• multimodal fusion
• salient and camouflaged object detection