10 papers
Revisiting Human-in-the-Loop Object Retrieval with Pre-Trained Vision Transformers
Kawtar Zaher, Olivier Buisson, Alexis Joly
Building on existing approaches, we revisit Human-in-the-Loop Object Retrieval, a task that consists of iteratively retrieving images containing objects of a class-of-interest, spe…
Positive-First Most Ambiguous: A Simple Active Learning Criterion for Interactive Retrieval of Rare Categories
Kawtar Zaher, Olivier Buisson, Alexis Joly
Real-world fine-grained visual retrieval often requires discovering a rare concept from large unlabeled collections with minimal supervision. This is especially critical in biodive…
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…
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…
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…