13 citations · 17 across the 18 of their papers we have counts for
9 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…
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).…
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…