1 citations · 1 across the 6 of their papers we have counts for
4 papers · 1 filter
TACO: Training-free Sound Prompted Segmentation via Semantically Constrained Audio-visual CO-factorization
Hugo Malard, Michel Olvera, Stephane Lathuiliere +1
Large-scale pre-trained audio and image models demonstrate an unprecedented degree of generalization, making them suitable for a wide range of applications. Here, we tackle the spe…
An Eye for an Ear: Zero-shot Audio Description Leveraging an Image Captioner using Audiovisual Distribution Alignment
Hugo Malard, Michel Olvera, Stéphane Lathuiliere +1
Multimodal large language models have fueled progress in image captioning. These models, fine-tuned on vast image datasets, exhibit a deep understanding of semantic concepts. In th…
A sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification
Michel Olvera, Paraskevas Stamatiadis, Slim Essid
Audio-text models trained via contrastive learning offer a practical approach to perform audio classification through natural language prompts, such as "this is a sound of" followe…
SALT: Standardized Audio event Label Taxonomy
Paraskevas Stamatiadis, Michel Olvera, Slim Essid
Machine listening systems often rely on fixed taxonomies to organize and label audio data, key for training and evaluating deep neural networks (DNNs) and other supervised algorith…