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pro vyhledávání: '"Sahbi, Hichem"'
Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is transfer learning which consists in pretraining a detection model in a sou
Externí odkaz:
http://arxiv.org/abs/2402.09315
Autor:
Sahbi, Hichem
In this paper, we devise a novel lightweight Graph Convolutional Network (GCN) design dubbed as Multi-Rate Magnitude Pruning (MRMP) that jointly trains network topology and weights. Our method is variational and proceeds by aligning the weight distri
Externí odkaz:
http://arxiv.org/abs/2312.17615
Autor:
Sahbi, Hichem
We introduce a novel interactive satellite image change detection algorithm based on active learning. The proposed method is iterative and consists in frugally probing the user (oracle) about the labels of the most critical images, and according to t
Externí odkaz:
http://arxiv.org/abs/2312.16965
Self-supervised monocular depth estimation methods aim to be used in critical applications such as autonomous vehicles for environment analysis. To circumvent the potential imperfections of these approaches, a quantification of the prediction confide
Externí odkaz:
http://arxiv.org/abs/2311.06137
Autor:
Sahbi, Hichem, Deschamps, Sebastien
Change detection in satellite imagery seeks to find occurrences of targeted changes in a given scene taken at different instants. This task has several applications ranging from land-cover mapping, to anthropogenic activity monitory as well as climat
Externí odkaz:
http://arxiv.org/abs/2309.14781
Autor:
Sahbi, Hichem
Magnitude pruning is one of the mainstream methods in lightweight architecture design whose goal is to extract subnetworks with the largest weight connections. This method is known to be successful, but under very high pruning regimes, it suffers fro
Externí odkaz:
http://arxiv.org/abs/2306.17590
Autor:
Sahbi, Hichem
Graph convolutional networks (GCNs) are nowadays becoming mainstream in solving many image processing tasks including skeleton-based recognition. Their general recipe consists in learning convolutional and attention layers that maximize classificatio
Externí odkaz:
http://arxiv.org/abs/2305.19343
Autor:
Sahbi, Hichem, Deschamps, Sebastien
Satellite image change detection aims at finding occurrences of targeted changes in a given scene taken at different instants. This task is highly challenging due to the acquisition conditions and also to the subjectivity of changes. In this paper, w
Externí odkaz:
http://arxiv.org/abs/2212.13974
Autor:
Sahbi, Hichem
In this paper, we design lightweight graph convolutional networks (GCNs) using a particular class of regularizers, dubbed as phase-field models (PFMs). PFMs exhibit a bi-phase behavior using a particular ultra-local term that allows training both the
Externí odkaz:
http://arxiv.org/abs/2212.09415
Autor:
Deschamps, Sebastien, Sahbi, Hichem
Most of the existing learning models, particularly deep neural networks, are reliant on large datasets whose hand-labeling is expensive and time demanding. A current trend is to make the learning of these models frugal and less dependent on large col
Externí odkaz:
http://arxiv.org/abs/2212.04868