Meta Research

In this work, we present a novel framework for computing visual counterfactual explanations based on two key ideas.

We introduce VSA-based constraints on adversarial learning for source-to-target translations by learning a hypervector mapping that inverts the translation to ensure consistency...

We propose a method for estimating the 6DoF pose of a rigid object with an available 3D model from a single RGB image. Unlike classical correspondence-based methods which predict...

In this paper, we advocate an end-to-end reinforcement learning (RL) paradigm to demonstrate how an agent can learn directly from machine-readable music score to play the piano...

This paper investigates self-supervised pre-training for audiovisual speaker representation learning where a visual stream showing the speaker’s mouth area is used alongside...

First, we propose a FL system for on-device ASR domain adaptation with full self-supervision, which uses self-labeling together with data augmentation and filtering techniques.

In this work, we present a self-supervised AVSR framework built upon Audio-Visual HuBERT (AV-HuBERT), a state-of-the-art audio-visual speech representation learning model.

We present a novel way of conditioning a pretrained denoising diffusion speech model to produce speech in the voice of a novel person unseen during training.

In this work, we propose a novel BIQA model, called GreenBIQA, that aims at high performance, low computational complexity and a small model size.

In addition to egocentric recordings made with AR glasses, extensive metadata, including the position and orientation of speakers, is provided. This paper describes the use...