Publications related to WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models

Related publications (10)

Learning transferable land cover semantics for open vocabulary interactions with remote sensing images

Devis Tuia, Javiera Francisca Castillo Navarro, Valérie Zermatten

Why should we confine land cover classes to rigid and arbitrary definitions? Land cover mapping is a central task in remote sensing image processing, but the rigorous class definitions can sometimes restrict the transferability of annotations between datas ...

2025

Robust and Personalized Federated Learning: Handling Data Corruption and Heterogeneity

Ljubomir Rokvic

Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling collaborative model training across decentralized devices while preserving data privacy. However, FL's success is highly contingent on the quality and integrity ...

EPFL2025

Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper

Juan Pablo Zuluaga Gomez

The training of automatic speech recognition (ASR) with little to no supervised data remains an open question. In this work, we demonstrate that streaming Transformer-Transducer (TT) models can be trained from scratch in consumer and accessible GPUs in the ...

Association for Computational Linguistics (ACL)2024

Privacy and Confidentiality in Machine Learning and Data Analysis: Understanding Risks and Developing Protections

Valentin Hartmann

Without the ability to collect, access and analyze data, most of nowadays research would be impossible. Without data to learn from, the field of machine learning (ML) would not exist. However, much of the particularly useful data---medical records, human b ...

EPFL2024

Constrained Zero-Shot Neural Architecture Search on Small Classification Dataset

Liza Carol Andrea Dunbar

The rapid evolution of Deep Learning (DL) has brought about significant transformations across scientific domains, marked by the development of increasingly intricate models demanding powerful GPU platforms. However, edge applications like wearables and mo ...

IEEE2024

Nuance Matters: Probing Epistemic Consistency in Causal Reasoning

Boi Faltings, Shaobo Cui, Luca Mouchel, Yiyang Feng

Previous research on causal reasoning often overlooks the subtleties crucial to understanding causal reasoning. To address this gap, our study introduces the concept of causal epistemic consistency, which focuses on the self-consistency of Large Language M ...

2024

Hierarchical Control for Vehicle Repositioning in Autonomous Mobility-on-Demand Systems

Nikolaos Geroliminis, Giancarlo Ferrari Trecate, Pengbo Zhu

Balancing passenger demand and vehicle availability is crucial for ensuring the sustainability and effectiveness of urban transportation systems. To address this challenge, we propose a novel hierarchical strategy for the efficient distribution of empty ve ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

Harnessing data science to improve molecular structure elucidation from tandem mass spectrometry

Fernando Perez Cruz, Guillaume Romain Obozinski, Marco Volpi

Compound structural identification for non-targeted screening of organic molecules in complex mixtures is commonly carried out using liquid chromatography coupled to tandem mass spectrometry (UHPLC-HRMS/MS and related techniques). Instrumental developments ...

2023

Facilitated machine learning for image-based fruit quality assessment

Fernando Perez Cruz

Image-based machine learning models can be used to make the sorting and grading of agricultural products more efficient. In many regions, implementing such systems can be difficult due to the lack of centralization and automation of postharvest supply chai ...

2023

Ab initio quantum chemistry with neural-network wavefunctions

Giuseppe Carleo

Deep learning methods outperform human capabilities in pattern recognition and data processing problems and now have an increasingly important role in scientific discovery. A key application of machine learning in molecular science is to learn potential en ...

NATURE PORTFOLIO2023