@conference{SmoothDiffusion_WACV_2026,

title = {SmoothDiffusion-VE: Real-time Generative Video Editing Using Adaptive Feature Cache},

author = {Mustafa Munir and Sophia Zalewski and Shiqiu Liu and David Tarjan and Sushmitha Belede and Anjul Patney and Radu Marculescu},

year  = {2026},

date = {2026-03-02},

booktitle = {2026 Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2026)},

keywords = {Edge AI, Efficient AI, Efficient Inference, Generative AI},

pubstate = {forthcoming},

tppubtype = {conference}

}

@conference{efficientmednext@rahman,

title = {EfficientMedNeXt: Multi-Receptive Dilated Convolutions for Medical Image Segmentation},

author = {Md Mostafijur Rahman and Mustafa Munir and Radu Marculescu},

url = {https://link.springer.com/chapter/10.1007/978-3-032-04965-0_19},

year  = {2025},

date = {2025-09-23},

urldate = {2025-09-23},

publisher = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},

abstract = {In this work, we introduce EfficientMedNeXt\textemdasha lightweight, high-performance segmentation architecture developed through a two-phase optimization process applied to the MedNeXt architecture. To this end, we first optimize the decoder by reducing the high-resolution redundancy and unifying the decoder channels across stages for enhanced efficiency. Then, we introduce a new Dilated Multi-Receptive Field Block (DMRFB) to capture the multi-scale spatial context efficiently without increasing the kernel sizes and relying on the channel expansion convolutions. Extensive evaluations on BTCV, FeTA, and MSD show that EfficientMedNeXt-L achieves 87.0% DICE score on BTCV (+1.04% over MedNeXt-L) with 96.5% fewer parameters and 77.03% lower FLOPs. In addition, EfficientMedNeXt-S offers comparable DICE score, improved HD95, and 78.1% higher throughput while reducing parameters by 98.5% and FLOPs by 95%. These results demonstrate EfficientMedNeXt’s efficiency and accuracy, making it well-suited for real-world clinical applications. Code will be released upon acceptance.},

keywords = {3D Segmentation, Deep Learning Architecture, Efficient AI, Efficient Inference, Featured, Medical Image Segmentation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Data_to_Design_Frequency,

title = {From Data to Design: Leveraging Frequency Statistics for Efficient Neural Network Architectures},

author = {Mustafa Munir and Guihong Li and Md Mostafijur Rahman and Alex Zhang and Radu Marculescu},

url = {https://openaccess.thecvf.com/content/CVPR2025W/eLVM/html/Munir_From_Data_to_Design_Leveraging_Frequency_Statistics_for_Efficient_Neural_CVPRW_2025_paper.html},

year  = {2025},

date = {2025-06-11},

urldate = {2025-06-11},

booktitle = {2025 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},

keywords = {Deep Learning Architecture, Efficient Inference, Model Compression \& Optimization, Neural Architecture Search},

pubstate = {published},

tppubtype = {conference}

}

@conference{liti,

title = {Demo Abstract: Lightweight Training and Inference for Self-Supervised Depth Estimation on Edge Devices},

author = {Allen-Jasmin Farcas and Radu Marculescu},

url = {https://dl.acm.org/doi/10.1145/3715014.3724373},

year  = {2025},

date = {2025-05-07},

urldate = {2025-05-07},

booktitle = {Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems (SenSys 2025)},

keywords = {Edge AI, Efficient Inference, Federated Learning, Monocular Depth Estimation, Self-Supervised Learning},

pubstate = {published},

tppubtype = {conference}

}

@conference{RapidNet_WACV_2025,

title = {RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone},

author = {Mustafa Munir and Md Mostafijur Rahman and Radu Marculescu},

url = {https://ieeexplore.ieee.org/document/10943953},

year  = {2025},

date = {2025-03-03},

urldate = {2025-03-03},

booktitle = {2025 Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2025)},

journal = {2025 Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2025)},

keywords = {Deep Learning Architecture, Edge AI, Efficient Inference, Featured, Model Compression \& Optimization},

pubstate = {published},

tppubtype = {conference}

}

@conference{ADA_VE_WACV_2025,

title = {Ada-VE: Training-Free Consistent Video Editing Using Adaptive Motion Prior},

author = {Tanvir Mahmud and Mustafa Munir and Radu Marculescu and Diana Marculescu},

url = {https://ieeexplore.ieee.org/document/10943436},

year  = {2025},

date = {2025-02-28},

urldate = {2025-02-28},

booktitle = {2025 Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2025)},

keywords = {Efficient Inference, Generative AI},

pubstate = {published},

tppubtype = {conference}

}

@conference{GreedyViG_CVPR_2024,

title = {GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNs},

author = {Mustafa Munir and William Avery and Md Mostafijur Rahman and Radu Marculescu},

url = {https://openaccess.thecvf.com/content/CVPR2024/papers/Munir_GreedyViG_Dynamic_Axial_Graph_Construction_for_Efficient_Vision_GNNs_CVPR_2024_paper.pdf},

year  = {2024},

date = {2024-06-19},

urldate = {2024-06-19},

booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

abstract = {Vision graph neural networks (ViG) offer a new avenue for exploration in computer vision. A major bottleneck in ViGs is the inefficient k-nearest neighbor (KNN) operation used for graph construction. To solve this issue, we propose a new method for designing ViGs, Dynamic Axial Graph Construction (DAGC), which is more efficient than KNN as it limits the number of considered graph connections made within an image. Additionally, we propose a novel CNN-GNN architecture, GreedyViG, which uses DAGC. Extensive experiments show that GreedyViG beats existing ViG, CNN, and ViT architectures in terms of accuracy, GMACs, and parameters on image classification, object detection, instance segmentation, and semantic segmentation tasks. Our smallest model, GreedyViG-S, achieves 81.1% top-1 accuracy on ImageNet-1K, 2.9% higher than Vision GNN and 2.2% higher than Vision HyperGraph Neural Network (ViHGNN), with less GMACs and a similar number of parameters. Our largest model, GreedyViG-B obtains 83.9% top-1 accuracy, 0.2% higher than Vision GNN, with a 66.6% decrease in parameters and a 69% decrease in GMACs. GreedyViG-B also obtains the same accuracy as ViHGNN with a 67.3% decrease in parameters and a 71.3% decrease in GMACs. Our work shows that hybrid CNNGNN architectures not only provide a new avenue for designing efficient models, but that they can also exceed the performance of current state-of-the-art models.},

keywords = {Deep Learning Architecture, Dynamic networks, Edge AI, Efficient Inference, Graph Neural Network},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey,

title = {Beyond Thresholds: A General Approach to Sensor Selection for Practical Deep Learning-based HAR},

author = {Geffen Cooper and Radu Marculescu},

url = {https://ieeexplore.ieee.org/abstract/document/10562185},

year  = {2024},

date = {2024-05-13},

urldate = {2024-05-13},

booktitle = {Proceedings of the 9th ACM/IEEE Conference on Internet of Things Design and Implementation},

keywords = {Cascaded Inference, Efficient Inference, Human Activity Recognition, Sensor Selection},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey,

title = {Demo Abstract: Online Training and Inference for On-Device Monocular Depth Estimation},

author = {Allen-Jasmin Farcas and Geffen Cooper and Hyun Joon Song and Afnan Mir and Vincent Liew and Chloe Tang and Prithvi Senthilkumar and Tiani Chen-Troester and Radu Marculescu},

url = {https://ieeexplore.ieee.org/abstract/document/10562188},

year  = {2024},

date = {2024-05-13},

booktitle = {Proceedings of the 9th ACM/IEEE Conference on Internet of Things Design and Implementation},

keywords = {Edge AI, Efficient Inference, Monocular Depth Estimation, On-device Training},

pubstate = {published},

tppubtype = {conference}

}