arXiv Computer Vision
Rethinking Camouflage Image Generation towards a Training-Free Paradigm
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To Blend In, First Decouple: Rethinking Camouflage Image Generation via Context-Decoupled Representations
arXiv:2607.17768v2 Announce Type: replace Abstract: Camouflage image generation (CIG) focuses on generating visually concealed objects that seamlessly blend into their backgrounds. Existing methods t...
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Phantom-Insight: Adaptive Multi-cue Fusion for Video Camouflaged Object Detection with Multimodal LLM
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EReCu: Pseudo-label Evolution Fusion and Refinement with Multi-Cue Learning for Unsupervised Camouflage Detection
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Domain shift-robust object detection with GenAI image editing
The paper investigates using diffusion-based generative image editing to improve object detector robustness against domain shifts, specifically camouflaged military vehicle detection. By synthetically adding foliage, netting, and multi‑spectral camouflage to training data with models such as Qwen Image Edit 2509 and Flux.2 Dev, the authors demonstrate significant mAP gains (up to +20.1 for foliage) over detectors trained on uncamouflaged data. LoRA fine‑tuning further boosts performance for the more challenging multi‑spectral camouflage.
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UniVVT: A Unified End-to-End Framework for High-Fidelity Video Virtual Try-on
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arXiv Computer Vision
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