79 citations · 355 across the 59 of their papers we have counts for
14 papers · 1 filter
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…
Mitigating Intra- and Inter-modal Forgetting in Continual Learning of Unified Multimodal Models
Xiwen Wei, Mustafa Munir, Radu Marculescu
Unified Multimodal Generative Models (UMGMs) unify visual understanding and image generation within a single autoregressive framework. However, their ability to continually learn n…
Single-Round Scalable Analytic Federated Learning
Alan T. L. Bacellar, Mustafa Munir, Felipe M. G. França +3
Federated Learning (FL) is plagued by two key challenges: high communication overhead and performance collapse on heterogeneous (non-IID) data. Analytic FL (AFL) provides a single-…
ObjectAlign: Neuro-Symbolic Object Consistency Verification and Correction
Mustafa Munir, Harsh Goel, Xiwen Wei +6
Video editing and synthesis often introduce object inconsistencies, such as frame flicker and identity drift that degrade perceptual quality. To address these issues, we introduce…
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…
Multi-Scale High-Resolution Logarithmic Grapher Module for Efficient Vision GNNs
Mustafa Munir, Alex Zhang, Radu Marculescu
Vision graph neural networks (ViG) have demonstrated promise in vision tasks as a competitive alternative to conventional convolutional neural nets (CNN) and transformers (ViTs); h…
AttentionViG: Cross-Attention-Based Dynamic Neighbor Aggregation in Vision GNNs
Hakan Emre Gedik, Andrew Martin, Mustafa Munir +4
Vision Graph Neural Networks (ViGs) have demonstrated promising performance in image recognition tasks against Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs).…
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…
VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation
Mustafa Munir, Alex Zhang, Radu Marculescu
Recent advances in Vision Transformers (ViTs) and State Space Models (SSMs) have challenged the dominance of Convolutional Neural Networks (CNNs) in computer vision. ViTs excel at…
NeuS-QA: Grounding Long-Form Video Understanding in Temporal Logic and Neuro-Symbolic Reasoning
Sahil Shah, S P Sharan, Harsh Goel +5
While vision-language models (VLMs) excel at tasks involving single images or short videos, they still struggle with Long Video Question Answering (LVQA) due to its demand for comp…
The Effect of Network Topology on the Equilibria of Influence-Opinion Games
Yigit Ege Bayiz, Arash Amini, Radu Marculescu +1
Online social networks exert a powerful influence on public opinion. Adversaries weaponize these networks to manipulate discourse, underscoring the need for more resilient social n…
Simulating Rumor Spreading in Social Networks using LLM Agents
Tianrui Hu, Dimitrios Liakopoulos, Xiwen Wei +2
With the rise of social media, misinformation has become increasingly prevalent, fueled largely by the spread of rumors. This study explores the use of Large Language Model (LLM) a…
Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data
Sofia Hurtado, Radu Marculescu
For rapidly spreading diseases where many cases show no symptoms, swift and effective contact tracing is essential. While exposure notification applications provide alerts on poten…