1 citations · 1 across the 10 of their papers we have counts for
11 papers
Test Time Adaptation Methods for Point Cloud Registration in Laparoscopic Surgery
Nina Bodelot, Soufiane Belharbi, Eric Granger
3D point cloud registration in laparoscopic surgery estimates the transformation between an intraoperative organ reconstructed from video and its preoperative mesh. Because ground-…
Continuous Adversarial MeanFlow Transfer
Yara Bahram, Zahra Dehghani, Mélodie Desbos +3
Training fast generators on new domains with limited data remains challenging for two reasons. First, adapting a pretrained diffusion or flow model to a new domain leaves its costl…
A Production-Oriented Framework for Evaluation of SFX Generation
Mélodie Desbos, Yara Bahram, Eric Granger +1
Industrial sound design requires audio generation systems that not only produce realistic audio, but also preserve the perceptual identity of a reference, support controllable vari…
Selective Prediction Forgetting for Adaptation of Weakly Supervised Localization of Histology Images
Alexis Guichemerre, Banafsheh Karimian, Soufiane Belharbi +6
Under strong cross-domain shifts, weakly supervised object localization (WSOL) models often lose their capacity to discriminate and develop a strong prediction bias toward a subset…
Longitudinal Risk Prediction in Mammography with Privileged History Distillation
Banafsheh Karimian, Soufiane Belharbi, Alexis Guichemerre +3
Longitudinal mammography screening has become an important source of information for improving future breast cancer risk prediction. However, the performance of current longitudina…
Uni-DAD: Unified Distillation and Adaptation of Diffusion Models for Few-step Few-shot Image Generation
Yara Bahram, Mélodie Desbos, Mohammadhadi Shateri +1
Diffusion models (DMs) produce high-quality images, yet their sampling remains costly when adapted to new domains. Distilled DMs are faster but typically remain confined within the…
Learning Task-Agnostic Representations through Multi-Teacher Distillation
Philippe Formont, Maxime Darrin, Banafsheh Karimian +5
Casting complex inputs into tractable representations is a critical step across various fields. Diverse embedding models emerge from differences in architectures, loss functions, i…
DogFit: Domain-guided Fine-tuning for Efficient Transfer Learning of Diffusion Models
Yara Bahram, Mohammadhadi Shateri, Eric Granger
Transfer learning of diffusion models to smaller target domains is challenging, as naively fine-tuning the model often results in poor generalization. Test-time guidance methods he…
CLIP-IT: CLIP-based Pairing for Histology Images Classification
Banafsheh Karimian, Giulia Avanzato, Soufian Belharbi +4
Multimodal learning has shown promise in medical imaging, combining complementary modalities like images and text. Vision-language models (VLMs) capture rich diagnostic cues but of…
PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization
Alexis Guichemerre, Soufiane Belharbi, Mohammadhadi Shateri +2
Weakly supervised object localization (WSOL) methods allow training models to classify images and localize ROIs. WSOL only requires low-cost image-class annotations yet provides a…
MTLoc: A Confidence-Based Source-Free Domain Adaptation Approach For Indoor Localization
Negar Mehregan, Berk Bozkurt, Eric Granger +2
Various deep learning models have been developed for indoor localization based on radio-frequency identification (RFID) tags. However, they often require adaptation to ensure accur…