no code implementations • 20 Mar 2023 • Nathan Hubens, Victor Delvigne, Matei Mancas, Bernard Gosselin, Marius Preda, Titus Zaharia
The advent of sparsity inducing techniques in neural networks has been of a great help in the last few years.
1 code implementation • 20 May 2022 • Ratha Siv, Matei Mancas, Bernard Gosselin, Dona Valy, Sokchenda Sreng
We use a well-known bounding box detector YOLO (v4) for the detection to compare to OpenPose which was used in our last paper, and we use SORT and DeepSORT to compare to centroid which was also used previously, and most importantly for the re-identification, we use a bunch of deep leaning methods such as MLFN, OSNet, and OSNet-AIN with our custom classification layer to compare to FaceNet which was also used earlier in our last paper.
no code implementations • 11 Mar 2022 • Nathan Hubens, Matei Mancas, Bernard Gosselin, Marius Preda, Titus Zaharia
This technique ensures that the criteria of selection focuses on redundant filters, while retaining the rare ones, thus maximizing the variety of remaining filters.
no code implementations • 15 Dec 2021 • Nathan Hubens, Matei Mancas, Bernard Gosselin, Marius Preda, Titus Zaharia
Neural networks usually involve a large number of parameters, which correspond to the weights of the network.
no code implementations • 23 Sep 2021 • Phutphalla Kong, Matei Mancas, Bernard Gosselin, Kimtho Po
In this paper, we propose a new visual attention model called DeepRare2021 (DR21) which uses the power of DNNs feature extraction and the genericity of feature-engineered algorithms.
1 code implementation • 5 Jul 2021 • Nathan Hubens, Matei Mancas, Bernard Gosselin, Marius Preda, Titus Zaharia
Most of the time, sparsity is introduced using a three-stage pipeline: 1) train the model to convergence, 2) prune the model according to some criterion, 3) fine-tune the pruned model to recover performance.
no code implementations • 22 Jul 2013 • Nicolas Riche, Matthieu Duvinage, Matei Mancas, Bernard Gosselin, Thierry Dutoit
In this paper, a new framework is proposed to assess models of visual saliency.