2 citations · 2 across the 5 of their papers we have counts for
6 papers
AC3S: Adaptive Conditioning for 3D-Aware Synthetic Data Generation
Eric Ji, Qiran Hu, Wufei Ma +4
Synthetic data generation has emerged as a powerful tool for improving data scalability in computer vision. Recent diffusion-based pipelines have demonstrated strong photorealism.…
UniCon: Unified Framework for Efficient Contrastive Alignment via Kernels
Hangke Sui, Yuqing Wang, Minh N Do
Contrastive objectives power state-of-the-art multimodal models, but their training remains slow, relying on long stochastic optimization. We propose a Unified Framework for Effici…
Towards Achieving Perfect Multimodal Alignment
Abhi Kamboj, Minh N. Do
Multimodal alignment constructs a joint latent vector space where modalities representing the same concept map to neighboring latent vectors. We formulate this as an inverse proble…
GIST: Towards Photorealistic Style Transfer via Multiscale Geometric Representations
Renan A. Rojas-Gomez, Minh N. Do
State-of-the-art Style Transfer methods often leverage pre-trained encoders optimized for discriminative tasks, which may not be ideal for image synthesis. This can result in signi…
C3T: Cross-modal Transfer Through Time for Sensor-based Human Activity Recognition
Abhi Kamboj, Anh Duy Nguyen, Minh N. Do
In order to unlock the potential of diverse sensors, we investigate a method to transfer knowledge between time-series modalities using a multimodal \textit{temporal} representatio…
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition
Abhi Kamboj, Minh Do
Despite living in a multi-sensory world, most AI models are limited to textual and visual understanding of human motion and behavior. In fact, full situational awareness of human m…