Search Results for author:
Found 9 papers, 6 papers with code
Dimma: Semi-supervised Low Light Image Enhancement with Adaptive Dimming
To fill this gap, we propose Dimma, a semi-supervised approach that aligns with any camera by utilizing a small set of image pairs to replicate scenes captured under extreme lighting conditions taken by that specific camera.
Self-supervised adversarial masking for 3D point cloud representation learning
We introduce PointCAM, a novel adversarial method for learning a masking function for point clouds.
Speech Driven Video Editing via an Audio-Conditioned Diffusion Model
Taking inspiration from recent developments in visual generative tasks using diffusion models, we propose a method for end-to-end speech-driven video editing using a denoising diffusion model.
Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation
Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos.
Continual learning on 3D point clouds with random compressed rehearsal
By using rehearsal and reconstruction as regularization methods of the learning process, our approach achieves a significant decrease of catastrophic forgetting compared to the existing solutions on several most popular point cloud datasets considering two continual learning settings: when a task is known beforehand, and in the challenging scenario of when task information is unknown to the model.
Information Retrieval for ZeroSpeech 2021: The Submission by University of Wroclaw
We present a number of low-resource approaches to the tasks of the Zero Resource Speech Challenge 2021.
Aligned Contrastive Predictive Coding
We investigate the possibility of forcing a self-supervised model trained using a contrastive predictive loss to extract slowly varying latent representations.
Representing Point Clouds with Generative Conditional Invertible Flow Networks
To exploit similarities between same-class objects and to improve model performance, we turn to weight sharing: networks that model densities of points belonging to objects in the same family share all parameters with the exception of a small, object-specific embedding vector.
Conditional Invertible Flow for Point Cloud Generation
This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models.
