28 citations · 51 across the 11 of their papers we have counts for
12 papers
PipeFlow: Pipelined Processing and Motion-Aware Frame Selection for Long-Form Video Editing
Mustafa Munir, Md Mostafijur Rahman, Kartikeya Bhardwaj +2
Long-form video editing poses unique challenges due to the exponential increase in the computational cost from joint editing and Denoising Diffusion Implicit Models (DDIM) inversio…
AdaptViG: Adaptive Vision GNN with Exponential Decay Gating
Mustafa Munir, Md Mostafijur Rahman, Radu Marculescu
Vision Graph Neural Networks (ViGs) offer a new direction for advancements in vision architectures. While powerful, ViGs often face substantial computational challenges stemming fr…
LoMix: Learnable Weighted Multi-Scale Logits Mixing for Medical Image Segmentation
Md Mostafijur Rahman, Radu Marculescu
U-shaped networks output logits at multiple spatial scales, each capturing a different blend of coarse context and fine detail. Yet, training still treats these logits in isolation…
MK-UNet: Multi-kernel Lightweight CNN for Medical Image Segmentation
Md Mostafijur Rahman, Radu Marculescu
In this paper, we introduce MK-UNet, a paradigm shift towards ultra-lightweight, multi-kernel U-shaped CNNs tailored for medical image segmentation. Central to MK-UNet is the multi…
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…
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…
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation
Debesh Jha, Nikhil Kumar Tomar, Koushik Biswas +11
Accurate segmentation of organs from abdominal CT scans is essential for clinical applications such as diagnosis, treatment planning, and patient monitoring. To handle challenges o…
BanglaNet: Bangla Handwritten Character Recognition using Ensembling of Convolutional Neural Network
Chandrika Saha, Md Mostafijur Rahman
Handwritten character recognition is a crucial task because of its abundant applications. The recognition task of Bangla handwritten characters is especially challenging because of…
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
Md Mostafijur Rahman, Radu Marculescu
In recent years, medical image segmentation has become an important application in the field of computer-aided diagnosis. In this paper, we are the first to propose a new graph con…
Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation
Md Mostafijur Rahman, Radu Marculescu
Transformers have shown great success in medical image segmentation. However, transformers may exhibit a limited generalization ability due to the underlying single-scale self-atte…