79 citations · 355 across the 59 of their papers we have counts for
64 papers
Ceiling of a Task: When Can a Transformer Succeed Without Its Chain of Thought?
Jiashu He, Jinxuan Fan, Xiao Xiao +2
Reasoning models generate long chains of thought before they answer, yet it is debated whether the content of these chains does real computational work or is largely decorative. We…
Pareto LoRA: Mitigating Modality Imbalance in Unified Multimodal Models via Pareto-Optimal Gradient Integration
Xiwen Wei, Mark Nutter, Madhusudhanan Srinivasan +1
Unified multimodal models (UMMs) have recently emerged as a promising paradigm for integrating multimodal understanding and generation within a single autoregressive transformer. H…
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…
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…
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…
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…