18 citations · 69 across the 60 of their papers we have counts for
6 papers · 1 filter
DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations
Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb, Bruno Ribeiro +2
Pre-trained Vision Transformers now serve as powerful tools for computer vision. Yet, efficiently adapting them for multiple tasks remains a challenge that arises from the need to…
Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks
Moshe Eliasof, Nir Ben Zikri, Eran Treister
Unsupervised image segmentation is an important task in many real-world scenarios where labelled data is of scarce availability. In this paper we propose a novel approach that harn…
Rethinking Unsupervised Neural Superpixel Segmentation
Moshe Eliasof, Nir Ben Zikri, Eran Treister
Recently, the concept of unsupervised learning for superpixel segmentation via CNNs has been studied. Essentially, such methods generate superpixels by convolutional neural network…
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks
Moshe Eliasof, Benjamin Bodner, Eran Treister
Graph Convolutional Networks (GCNs) are widely used in a variety of applications, and can be seen as an unstructured version of standard Convolutional Neural Networks (CNNs). As in…
MGIC: Multigrid-in-Channels Neural Network Architectures
Moshe Eliasof, Jonathan Ephrath, Lars Ruthotto +1
We present a multigrid-in-channels (MGIC) approach that tackles the quadratic growth of the number of parameters with respect to the number of channels in standard convolutional ne…
DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling
Moshe Eliasof, Eran Treister
Graph Convolutional Networks (GCNs) have shown to be effective in handling unordered data like point clouds and meshes. In this work we propose novel approaches for graph convoluti…