5 papers
VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detection
Narges Rashvand, Ghazal Alinezhad Noghre, Shanle Yao +2
Video Anomaly Detection (VAD) is inherently challenging due to the scarcity of anomalies and the large visual variability in surveillance footage, including changes in lighting, vi…
Distributed learning for automatic modulation recognition in bandwidth-limited networks
Narges Rashvand, Kenneth Witham, Gabriel Maldonado +4
Automatic Modulation Recognition (AMR) is critical in identifying various modulation types in wireless communication systems. Recent advancements in deep learning have facilitated…
Adversarially-Refined VQ-GAN with Dense Motion Tokenization for Spatio-Temporal Heatmaps
Gabriel Maldonado, Narges Rashvand, Armin Danesh Pazho +3
Continuous human motion understanding remains a core challenge in computer vision due to its high dimensionality and inherent redundancy. Efficient compression and representation a…
MoCLIP: Motion-Aware Fine-Tuning and Distillation of CLIP for Human Motion Generation
Gabriel Maldonado, Armin Danesh Pazho, Ghazal Alinezhad Noghre +2
Human motion generation is essential for fields such as animation, robotics, and virtual reality, requiring models that effectively capture motion dynamics from text descriptions.…
MoFM: A Large-Scale Human Motion Foundation Model
Mohammadreza Baharani, Ghazal Alinezhad Noghre, Armin Danesh Pazho +2
Foundation Models (FM) have increasingly drawn the attention of researchers due to their scalability and generalization across diverse tasks. Inspired by the success of FMs and the…