✨RF-DETR: Neural Architecture Search for Real-Time Detection Transformers

📝 Summary:
RF-DETR is a light-weight detection transformer leveraging weight-sharing NAS to optimize accuracy-latency tradeoffs across diverse datasets. It significantly outperforms prior state-of-the-art, being the first real-time detector to surpass 60 AP on COCO.

🔹 Publication Date: Published on Nov 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.09554
• PDF: https://arxiv.org/pdf/2511.09554

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#ObjectDetection #ComputerVision #MachineLearning #NeuralArchitectureSearch #Transformers

✨Taming Generative Synthetic Data for X-ray Prohibited Item Detection

📝 Summary:
Xsyn introduces a one-stage text-to-image synthesis pipeline for X-ray security images. It eliminates labor costs and improves image quality and efficiency for training detection models. This method significantly enhances prohibited item detection performance, outperforming prior approaches.

🔹 Publication Date: Published on Nov 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15299
• PDF: https://arxiv.org/pdf/2511.15299
• Github: https://github.com/pILLOW-1/Xsyn/

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#XraySecurity #GenerativeAI #ComputerVision #SyntheticData #ObjectDetection

✨MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection

📝 Summary:
MSRNet proposes a Multi-Scale Recursive Network for camouflaged object detection. It uses a Pyramid Vision Transformer and recursive feature refinement to overcome challenges with small and multiple objects, achieving state-of-the-art results.

🔹 Publication Date: Published on Nov 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12810
• PDF: https://arxiv.org/pdf/2511.12810

🔹 Models citing this paper:
• https://huggingface.co/linaa98/MSRNet

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#CamouflagedObjectDetection #ObjectDetection #ComputerVision #DeepLearning #AIResearch

✨YOLO Meets Mixture-of-Experts: Adaptive Expert Routing for Robust Object Detection

📝 Summary:
A new Mixture-of-Experts framework uses adaptive routing among multiple YOLOv9-T experts. This improves object detection performance, achieving higher mAP and AR.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13344
• PDF: https://arxiv.org/pdf/2511.13344

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#ObjectDetection #YOLO #MixtureOfExperts #DeepLearning #ComputerVision

✨Real-Time Object Detection Meets DINOv3

📝 Summary:
DEIMv2 extends DEIM with DINOv3 features, achieving superior real-time object detection across GPU, edge, and mobile. It uses a Spatial Tuning Adapter and pruned HGNetv2 for diverse models, setting new state of the art with impressive performance-cost trade-offs.

🔹 Publication Date: Published on Sep 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.20787
• PDF: https://arxiv.org/pdf/2509.20787
• Project Page: https://intellindust-ai-lab.github.io/projects/DEIMv2/
• Github: https://github.com/Intellindust-AI-Lab/DEIMv2

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#ObjectDetection #RealTimeAI #ComputerVision #MachineLearning #EdgeAI

✨YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection

📝 Summary:
YOLO-Master proposes an Efficient Sparse Mixture-of-Experts ES-MoE block for real-time object detection. It adaptively allocates computational resources based on scene complexity using a dynamic routing network, overcoming static computation limits. This improves accuracy and speed, especially on...

🔹 Publication Date: Published on Dec 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23273
• PDF: https://arxiv.org/pdf/2512.23273

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#ObjectDetection #YOLO #MixtureOfExperts #Transformers #RealTimeAI