5 papers · 1 filter
Mem-MLP: Real-Time 3D Human Motion Generation from Sparse Inputs
Sinan Mutlu, Georgios F. Angelis, Savas Ozkan +3
Realistic and smooth full-body tracking is crucial for immersive AR/VR applications. Existing systems primarily track head and hands via Head Mounted Devices (HMDs) and controllers…
Multi-Task Pre-Finetuning of Lightweight Transformer Encoders for Text Classification and NER
Junyi Zhu, Savas Ozkan, Andrea Maracani +3
Deploying natural language processing (NLP) models on mobile platforms requires models that can adapt across diverse applications while remaining efficient in memory and computatio…
Efficient 3D Full-Body Motion Generation from Sparse Tracking Inputs with Temporal Windows
Georgios Fotios Angelis, Savas Ozkan, Sinan Mutlu +3
To have a seamless user experience on immersive AR/VR applications, the importance of efficient and effective Neural Network (NN) models is undeniable, since missing body parts tha…
Accurate Scene Text Recognition with Efficient Model Scaling and Cloze Self-Distillation
Andrea Maracani, Savas Ozkan, Sijun Cho +6
Scaling architectures have been proven effective for improving Scene Text Recognition (STR), but the individual contribution of vision encoder and text decoder scaling remain under…
Guided Model Merging for Hybrid Data Learning: Leveraging Centralized Data to Refine Decentralized Models
Junyi Zhu, Ruicong Yao, Taha Ceritli +6
Current network training paradigms primarily focus on either centralized or decentralized data regimes. However, in practice, data availability often exhibits a hybrid nature, wher…