GenCAMO: Scene-Graph Contextual Decoupling for Environment-aware and Mask-free Camouflage Image-Dense Annotation Generation

2026-01-03

arXiv.org

AI Summary

This work addresses the scarcity of high-quality, large-scale annotated camouflage imagery that hinders dense prediction tasks such as camouflaged object detection and open-vocabulary segmentation. To overcome this limitation, we propose an environment-aware, mask-free generative framework that leverages scene graph context disentanglement to jointly synthesize realistic multimodal camouflaged images along with their dense annotations—including depth maps, attribute descriptions, and textual prompts—thereby constructing GenCAMO-DB, the first large-scale synthetic dataset for this domain. Experimental results demonstrate that models trained on our synthesized data achieve significantly improved performance in complex camouflaged scenarios, validating both the effectiveness and generalization capability of the generated data.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Conceal dense prediction (CDP), especially RGB-D camouflage object detection and open-vocabulary camouflage object segmentation, plays a crucial role in advancing the understanding and reasoning of complex camouflage scenes. However, high-quality and large-scale camouflage datasets with dense annotation remain scarce due to expensive data collection and labeling costs. To address this challenge, we explore leveraging generative models to synthesize realistic camouflage image-dense data for training CDP models with fine-grained representations, prior knowledge, and auxiliary reasoning. Concretely, our contributions are threefold: (i) we introduce GenCAMO-DB, a large-scale camouflage dataset with multi-modal annotations, including depth maps, scene graphs, attribute descriptions, and text prompts; (ii) we present GenCAMO, an environment-aware and mask-free generative framework that produces high-fidelity camouflage image-dense annotations; (iii) extensive experiments across multiple modalities demonstrate that GenCAMO significantly improves dense prediction performance on complex camouflage scenes by providing high-quality synthetic data. The code and datasets will be released after paper acceptance.

Problem

• camouflage image
• dense annotation
• dataset scarcity
• conceal dense prediction
• RGB-D camouflage object detection

Innovation

• GenCAMO
• scene-graph contextual decoupling
• mask-free generation
• camouflage image synthesis
• dense annotation