3 accepted papers
Understanding human motion from video is essential for a range of applications including pose estimation mesh recovery and action recognition. While state-of-the-art methods predominantly rely on transformer-based architectures these approaches have limitations in practical scenarios. Transformers a…
Estimation of information theoretic quantities such as mutual information and its conditional variant has drawn interest in recent times owing to their multifaceted applications. Newly proposed neural estimators for these quantities have overcome severe drawbacks of classical $k$NN-based estimators…
The field of neural generative models is dominated by the highly successful Generative Adversarial Networks (GANs) despite their challenges, such as training instability and mode collapse. Auto-Encoders (AE) with regularized latent space provide an alternative framework for generative models, albeit…