# LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion

arXiv: https://arxiv.org/abs/2404.00292 · HTML: https://mlsanity.com/paper/2404.00292/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 32 of its 69 references that are in the map.
- First posted on arXiv 30 Mar 2024.

## Placed in

- Retrieval-augmented generation / [Multimodal RAG](https://mlsanity.com/rag/multimodal-rag/)

## Builds on (most cited first, 30 of 32)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Is synthetic data from generative models ready for image recognition?](https://mlsanity.com/paper/2210.07574/) (2022)
- [DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort](https://mlsanity.com/paper/2104.06490/) (2021)
- [Anabranch Network for Camouflaged Object Segmentation](https://mlsanity.com/paper/2105.09451/) (2021)
- [Concealed Object Detection](https://mlsanity.com/paper/2102.10274/) (2021)
- [Simultaneously Localize, Segment and Rank the Camouflaged Objects](https://mlsanity.com/paper/2103.04011/) (2021)
- [SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation](https://mlsanity.com/paper/1810.09091/) (2018)
- [DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models](https://mlsanity.com/paper/2303.11681/) (2023)
- [Meta-Sim: Learning to Generate Synthetic Datasets](https://mlsanity.com/paper/1904.11621/) (2019)
- [Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing](https://mlsanity.com/paper/1810.08705/) (2018)
- [BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations](https://mlsanity.com/paper/2201.04684/) (2022)
- [Open-vocabulary Object Segmentation with Diffusion Models](https://mlsanity.com/paper/2301.05221/) (2023)
- [Affective Image Content Analysis: Two Decades Review and New Perspectives](https://mlsanity.com/paper/2106.16125/) (2021)
- [Advances in Deep Concealed Scene Understanding](https://mlsanity.com/paper/2304.11234/) (2023)
- [Bridging Global Context Interactions for High-Fidelity Image Completion](https://mlsanity.com/paper/2104.00845/) (2021)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies](https://mlsanity.com/paper/2108.10152/) (2021)
- [The Making and Breaking of Camouflage](https://mlsanity.com/paper/2309.03899/) (2023)
- [Location-Free Camouflage Generation Network](https://mlsanity.com/paper/2203.09845/) (2022)
- [Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation](https://mlsanity.com/paper/2209.07069/) (2022)
- [aUToTrack: A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge](https://mlsanity.com/paper/1905.08758/) (2019)
- [Rethinking Polyp Segmentation from an Out-of-Distribution Perspective](https://mlsanity.com/paper/2306.07792/) (2023)

## Built on by (most cited first, 12 of 12)

- [A Survey of Camouflaged Object Detection and Beyond](https://mlsanity.com/paper/2408.14562/) (2024)
- [Deep Learning in Concealed Dense Prediction](https://mlsanity.com/paper/2504.10979/) (2025)
- [Beyond Single Images: Retrieval Self-Augmented Unsupervised Camouflaged Object Detection](https://mlsanity.com/paper/2510.18437/) (2025)
- [Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation](https://mlsanity.com/paper/2504.02180/) (2025)
- [Hand-Shadow Poser](https://mlsanity.com/paper/2505.07012/) (2025)
- [A-SDM: Accelerating Stable Diffusion through Model Assembly and Feature Inheritance Strategies](https://mlsanity.com/paper/2406.00210/) (2024)
- [Text-guided Controllable Diffusion for Realistic Camouflage Images Generation](https://mlsanity.com/paper/2511.20218/) (2025)
- [RealCamo: Boosting Real Camouflage Synthesis with Layout Controls and Textual-Visual Guidance](https://mlsanity.com/paper/2512.22974/) (2025)
- [GenCAMO: Scene-Graph Contextual Decoupling for Environment-aware and Mask-free Camouflage Image-Dense Annotation Generation](https://mlsanity.com/paper/2601.01181/) (2026)
- [SeamCam: Quantifying Seamless Camouflage via Multi-Cue Visual Detectability](https://mlsanity.com/paper/2605.16515/) (2026)
- [Rethinking Camouflage Image Generation towards a Training-Free Paradigm](https://mlsanity.com/paper/2609.14377/) (2026)
- [Beyond the Foreground: FOV-Aware Polyp Image Synthesis via Lesion-Guided Adaptive Mucosal Context Propagation](https://mlsanity.com/paper/2609.19966/) (2026)

## Abstract (arXiv)

Camouflaged vision perception is an important vision task with numerous practical applications. Due to the expensive collection and labeling costs, this community struggles with a major bottleneck that the species category of its datasets is limited to a small number of object species. However, the existing camouflaged generation methods require specifying the background manually, thus failing to extend the camouflaged sample diversity in a low-cost manner. In this paper, we propose a Latent Background Knowledge Retrieval-Augmented Diffusion (LAKE-RED) for camouflaged image generation. To our knowledge, our contributions mainly include: (1) For the first time, we propose a camouflaged generation paradigm that does not need to receive any background inputs. (2) Our LAKE-RED is the first knowledge retrieval-augmented method with interpretability for camouflaged generation, in which we propose an idea that knowledge retrieval and reasoning enhancement are separated explicitly, to alleviate the task-specific challenges. Moreover, our method is not restricted to specific foreground targets or backgrounds, offering a potential for extending camouflaged vision perception to more diverse domains. (3) Experimental results demonstrate that our method outperforms the existing approaches, generating more realistic camouflage images.