6 citations · 12 across the 3 of their papers we have counts for
5 papers
Graph Attention Network for Camera Relocalization on Dynamic Scenes
Mohamed Amine Ouali, Mohamed Bouguessa, Riadh Ksantini
We devise a graph attention network-based approach for learning a scene triangle mesh representation in order to estimate an image camera position in a dynamic environment. Previou…
Coarse-to-Fine Object Tracking Using Deep Features and Correlation Filters
Ahmed Zgaren, Wassim Bouachir, Riadh Ksantini
During the last years, deep learning trackers achieved stimulating results while bringing interesting ideas to solve the tracking problem. This progress is mainly due to the use of…
Adversarial Deep Embedded Clustering: on a better trade-off between Feature Randomness and Feature Drift
Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini
Clustering using deep autoencoders has been thoroughly investigated in recent years. Current approaches rely on simultaneously learning embedded features and clustering the data po…
Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction
Nairouz Mrabah, Naimul Mefraz Khan, Riadh Ksantini +1
In unsupervised learning, there is no apparent straightforward cost function that can capture the significant factors of variations and similarities. Since natural systems have smo…
A Novel Image-centric Approach Towards Direct Volume Rendering
Naimul Khan, Riadh Ksantini, Ling Guan
Transfer Function (TF) generation is a fundamental problem in Direct Volume Rendering (DVR). A TF maps voxels to color and opacity values to reveal inner structures. Existing TF to…