49 citations · 132 across the 23 of their papers we have counts for
8 papers · 1 filter
A Strong Baseline for Generalized Few-Shot Semantic Segmentation
Sina Hajimiri, Malik Boudiaf, Ismail Ben Ayed +1
This paper introduces a generalized few-shot segmentation framework with a straightforward training process and an easy-to-optimize inference phase. In particular, we propose a sim…
Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference
Ségolène Martin, Malik Boudiaf, Emilie Chouzenoux +2
Standard few-shot benchmarks are often built upon simplifying assumptions on the query sets, which may not always hold in practice. In particular, for each task at testing time, th…
Simplex Clustering via sBeta with Applications to Online Adjustment of Black-Box Predictions
Florent Chiaroni, Malik Boudiaf, Amar Mitiche +1
We explore clustering the softmax predictions of deep neural networks and introduce a novel probabilistic clustering method, referred to as k-sBetas. In the general context of clus…
Model-Agnostic Few-Shot Open-Set Recognition
Malik Boudiaf, Etienne Bennequin, Myriam Tami +4
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have few labeled samples, while simultaneously dete…
FHIST: A Benchmark for Few-shot Classification of Histological Images
Fereshteh Shakeri, Malik Boudiaf, Sina Mohammadi +4
Few-shot learning has recently attracted wide interest in image classification, but almost all the current public benchmarks are focused on natural images. The few-shot paradigm is…
Realistic Evaluation of Transductive Few-Shot Learning
Olivier Veilleux, Malik Boudiaf, Pablo Piantanida +1
Transductive inference is widely used in few-shot learning, as it leverages the statistics of the unlabeled query set of a few-shot task, typically yielding substantially better pe…
A Differential Entropy Estimator for Training Neural Networks
Georg Pichler, Pierre Colombo, Malik Boudiaf +2
Mutual Information (MI) has been widely used as a loss regularizer for training neural networks. This has been particularly effective when learn disentangled or compressed represen…
Parameter-free Online Test-time Adaptation
Malik Boudiaf, Romain Mueller, Ismail Ben Ayed +1
Training state-of-the-art vision models has become prohibitively expensive for researchers and practitioners. For the sake of accessibility and resource reuse, it is important to f…