13 citations · 17 across the 18 of their papers we have counts for
21 papers
Accelerating Video Diffusion via Training-Free Trajectory Routing
Mustafa Munir, Huy Vu, Shreyas Misra +7
Video diffusion is computationally expensive, as it requires executing a large model across many denoising steps. Even with step-distillation, inference remains expensive because e…
Can Graphs Help Vision SSMs See Better?
Dhruv Parikh, Anvitha Ramachandran, Haoyang Fan +3
Vision state space models inherit the efficiency and long-range modeling ability of Mamba-style selective scans. However, their performance depends critically on the representation…
Video Compression Meets Video Generation: Latent Inter-Frame Pruning with Attention Recovery
Dennis Menn, Yuedong Yang, Bokun Wang +6
Current video generation models suffer from high computational latency, making real-time applications prohibitively costly. In this paper, we address this limitation by exploiting…
Parameter Efficient Fine-tuning for Domain-specific Gastrointestinal Disease Recognition
Sanjaya Poudel, Nikita Kunwor, Raj Simkhada +3
Despite recent advancements in the field of medical image analysis with the use of pretrained foundation models, the issue of distribution shifts between cross-source images largel…
Fuel Gauge: Estimating Chain-of-Thought Length Ahead of Time in Large Multimodal Models
Yuedong Yang, Xiwen Wei, Mustafa Munir +1
Reasoning Large Multi-modality Models (LMMs) have become the de facto choice for many applications. However, these models rely on a Chain-of-Thought (CoT) process that is lengthy a…
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…
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…
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…
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…