79 citations · 355 across the 59 of their papers we have counts for
14 papers · 1 filter
RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone
Mustafa Munir, Md Mostafijur Rahman, Radu Marculescu
Vision transformers (ViTs) have dominated computer vision in recent years. However, ViTs are computationally expensive and not well suited for mobile devices; this led to the preva…
How Media Competition Fuels the Spread of Misinformation
Arash Amini, Yigit Ege Bayiz, Eun-Ju Lee +3
Competition among news sources may encourage some sources to share fake news and misinformation to influence the public. While sharing misinformation may lead to a short-term gain…
Online-LoRA: Task-free Online Continual Learning via Low Rank Adaptation
Xiwen Wei, Guihong Li, Radu Marculescu
Catastrophic forgetting is a significant challenge in online continual learning (OCL), especially for non-stationary data streams that do not have well-defined task boundaries. Thi…
Skip2-LoRA: A Lightweight On-device DNN Fine-tuning Method for Low-cost Edge Devices
Hiroki Matsutani, Masaaki Kondo, Kazuki Sunaga +1
This paper proposes Skip2-LoRA as a lightweight fine-tuning method for deep neural networks to address the gap between pre-trained and deployed models. In our approach, trainable L…
CrediRAG: Network-Augmented Credibility-Based Retrieval for Misinformation Detection in Reddit
Ashwin Ram, Yigit Ege Bayiz, Arash Amini +2
Fake news threatens democracy and exacerbates the polarization and divisions in society; therefore, accurately detecting online misinformation is the foundation of addressing this…
A Tiny Supervised ODL Core with Auto Data Pruning for Human Activity Recognition
Hiroki Matsutani, Radu Marculescu
In this paper, we introduce a low-cost and low-power tiny supervised on-device learning (ODL) core that can address the distributional shift of input data for human activity recogn…
Scaling Graph Convolutions for Mobile Vision
William Avery, Mustafa Munir, Radu Marculescu
To compete with existing mobile architectures, MobileViG introduces Sparse Vision Graph Attention (SVGA), a fast token-mixing operator based on the principles of GNNs. However, Mob…
Ada-VE: Training-Free Consistent Video Editing Using Adaptive Motion Prior
Tanvir Mahmud, Mustafa Munir, Radu Marculescu +1
Video-to-video synthesis poses significant challenges in maintaining character consistency, smooth temporal transitions, and preserving visual quality during fast motion. While rec…
PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentation
Md Mostafijur Rahman, Mustafa Munir, Debesh Jha +2
The Segment Anything Model (SAM), originally designed for general-purpose segmentation tasks, has been used recently for polyp segmentation. Nonetheless, fine-tuning SAM with data…
EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
Md Mostafijur Rahman, Mustafa Munir, Radu Marculescu
An efficient and effective decoding mechanism is crucial in medical image segmentation, especially in scenarios with limited computational resources. However, these decoding mechan…
GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNs
Mustafa Munir, William Avery, Md Mostafijur Rahman +1
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…
Susceptibility of Communities against Low-Credibility Content in Social News Websites
Yigit Ege Bayiz, Arash Amini, Radu Marculescu +1
Social news websites, such as Reddit, have evolved into prominent platforms for sharing and discussing news. A key issue on social news websites sites is the formation of echo cham…
News Source Credibility Assessment: A Reddit Case Study
Arash Amini, Yigit Ege Bayiz, Ashwin Ram +2
In the era of social media platforms, identifying the credibility of online content is crucial to combat misinformation. We present the CREDiBERT (CREDibility assessment using Bi-d…
Machine Unlearning for Image-to-Image Generative Models
Guihong Li, Hsiang Hsu, Chun-Fu Chen +1
Machine unlearning has emerged as a new paradigm to deliberately forget data samples from a given model in order to adhere to stringent regulations. However, existing machine unlea…