Retrospective Memory for Camouflaged Object Detection

2025-06-18

AI Summary

Existing camouflage object detection (COD) methods predominantly rely on static feedforward architectures, lacking explicit modeling of historical context—thus struggling to identify subtle camouflage patterns under weak contrast or strong interference. To address this, we propose RetroMem, a retrospective memory-driven dynamic perception architecture featuring a novel two-stage memory mechanism: (1) a learning stage employing lightweight Dense Multi-scale Adapters (DMA) to enhance feature representation; and (2) a recall stage leveraging Dynamic Memory Mechanism (DMM) and Inference Pattern Reconstruction (IPR) for real-time, history-guided inference. The resulting retrospective-enhanced encoder–decoder framework achieves significant improvements over state-of-the-art methods across multiple mainstream COD benchmarks—particularly excelling in complex backgrounds and low-contrast camouflage scenarios. This demonstrates the critical importance of historical context modeling for robust COD.

Technology Category

• Computer Vision: Image and Video Retrieval
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Camouflaged object detection (COD) primarily focuses on learning subtle yet discriminative representations from complex scenes. Existing methods predominantly follow the parametric feedforward architecture based on static visual representation modeling. However, they lack explicit mechanisms for acquiring historical context, limiting their adaptation and effectiveness in handling challenging camouflage scenes. In this paper, we propose a recall-augmented COD architecture, namely RetroMem, which dynamically modulates camouflage pattern perception and inference by integrating relevant historical knowledge into the process. Specifically, RetroMem employs a two-stage training paradigm consisting of a learning stage and a recall stage to construct, update, and utilize memory representations effectively. During the learning stage, we design a dense multi-scale adapter (DMA) to improve the pretrained encoder's capability to capture rich multi-scale visual information with very few trainable parameters, thereby providing foundational inferences. In the recall stage, we propose a dynamic memory mechanism (DMM) and an inference pattern reconstruction (IPR). These components fully leverage the latent relationships between learned knowledge and current sample context to reconstruct the inference of camouflage patterns, thereby significantly improving the model's understanding of camouflage scenes. Extensive experiments on several widely used datasets demonstrate that our RetroMem significantly outperforms existing state-of-the-art methods.

Problem

• Enhancing camouflage object detection with historical context
• Dynamic memory integration for improved pattern perception
• Overcoming limitations in handling complex camouflage scenes

Innovation

• Two-stage training with learning and recall stages
• Dense multi-scale adapter for rich visual capture
• Dynamic memory mechanism for context-aware inference