7 citations · 9 across the 15 of their papers we have counts for
17 papers
Audio-to-Image Bird Species Retrieval without Audio-Image Pairs via Text Distillation
Ilyass Moummad, Marius Miron, Lukas Rauch +5
Audio-to-image retrieval offers an interpretable alternative to audio-only classification for bioacoustic species recognition, but learning aligned audio-image representations is c…
Self-Supervised Learning of Plant Image Representations
Ilyass Moummad, Kawtar Zaher, Hervé Goëau +3
Automated plant recognition plays a crucial role in biodiversity monitoring and conservation, yet current approaches rely heavily on supervised learning, which is limited by the av…
Energy-Efficient Plant Monitoring via Knowledge Distillation
Ilyass Moummad, Reda Bensaid, Kawtar Zaher +5
Recent advances in large-scale visual representation learning have significantly improved performance in plant species and plant disease recognition tasks. However, state-of-the-ar…
MUKA: Multi Kernel Audio Adaptation Of Audio-Language Models
Reda Bensaid, Amine Ouasfi, Yassir Bendou +4
Multimodal foundation models have demonstrated impressive generalization capabilities, yet efficiently adapting them to new tasks in a few-shot setting remains a critical challenge…
Self-Supervised Learning as Discrete Communication
Kawtar Zaher, Ilyass Moummad, Olivier Buisson +1
Most self-supervised learning (SSL) methods learn continuous visual representations by aligning different views of the same input, offering limited control over how information is…
Compact Hypercube Embeddings for Fast Text-based Wildlife Observation Retrieval
Ilyass Moummad, Marius Miron, David Robinson +7
Large-scale biodiversity monitoring platforms increasingly rely on multimodal wildlife observations. While recent foundation models enable rich semantic representations across visi…
Image Hashing via Cross-View Code Alignment in the Age of Foundation Models
Ilyass Moummad, Kawtar Zaher, Hervé Goëau +1
Efficient large-scale retrieval requires representations that are both compact and discriminative. Foundation models provide powerful visual and multimodal embeddings, but nearest…
Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classification
Lukas Rauch, René Heinrich, Houtan Ghaffari +4
Although probing frozen models has become a standard evaluation paradigm, self-supervised learning in audio defaults to fine-tuning when pursuing state-of-the-art on AudioSet. A ke…
Hashing-Baseline: Rethinking Hashing in the Age of Pretrained Models
Ilyass Moummad, Kawtar Zaher, Lukas Rauch +1
Information retrieval with compact binary embeddings, also referred to as hashing, is crucial for scalable fast search applications, yet state-of-the-art hashing methods require ex…
Can Masked Autoencoders Also Listen to Birds?
Lukas Rauch, René Heinrich, Ilyass Moummad +3
Masked Autoencoders (MAEs) learn rich semantic representations in audio classification through an efficient self-supervised reconstruction task. However, general-purpose models fai…
Acoustic identification of individual animals with hierarchical contrastive learning
Ines Nolasco, Ilyass Moummad, Dan Stowell +1
Acoustic identification of individual animals (AIID) is closely related to audio-based species classification but requires a finer level of detail to distinguish between individual…
Domain-Invariant Representation Learning of Bird Sounds
Ilyass Moummad, Romain Serizel, Emmanouil Benetos +1
Passive acoustic monitoring (PAM) is crucial for bioacoustic research, enabling non-invasive species tracking and biodiversity monitoring. Citizen science platforms provide large a…
Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds
Ilyass Moummad, Nicolas Farrugia, Romain Serizel +2
Multi-label imbalanced classification poses a significant challenge in machine learning, particularly evident in bioacoustics where animal sounds often co-occur, and certain sounds…
Self-Supervised Learning for Few-Shot Bird Sound Classification
Ilyass Moummad, Romain Serizel, Nicolas Farrugia
Self-supervised learning (SSL) in audio holds significant potential across various domains, particularly in situations where abundant, unlabeled data is readily available at no cos…
Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning
Ilyass Moummad, Romain Serizel, Nicolas Farrugia
Deep learning has been widely used recently for sound event detection and classification. Its success is linked to the availability of sufficiently large datasets, possibly with co…
Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection
Ilyass Moummad, Romain Serizel, Nicolas Farrugia
Bioacoustic sound event detection allows for better understanding of animal behavior and for better monitoring biodiversity using audio. Deep learning systems can help achieve this…
Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning
Ilyass Moummad, Nicolas Farrugia
Methods based on supervised learning using annotations in an end-to-end fashion have been the state-of-the-art for classification problems. However, they may be limited in their ge…