11 citations · 11 across the 2 of their papers we have counts for
7 papers
A deep cut into Split Federated Self-supervised Learning
Marcin Przewięźlikowski, Marcin Osial, Bartosz Zieliński +1
Collaborative self-supervised learning has recently become feasible in highly distributed environments by dividing the network layers between client devices and a central server. H…
LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision
Mateusz Pach, Dawid Rymarczyk, Koryna Lewandowska +2
Prototypical parts networks combine the power of deep learning with the explainability of case-based reasoning to make accurate, interpretable decisions. They follow the this looks…
AdaGlimpse: Active Visual Exploration with Arbitrary Glimpse Position and Scale
Adam Pardyl, Michał Wronka, Maciej Wołczyk +3
Active Visual Exploration (AVE) is a task that involves dynamically selecting observations (glimpses), which is critical to facilitate comprehension and navigation within an enviro…
Divide and not forget: Ensemble of selectively trained experts in Continual Learning
Grzegorz Rypeść, Sebastian Cygert, Valeriya Khan +3
Class-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know. A trend in this area is to use a mixtu…
TORE: Token Recycling in Vision Transformers for Efficient Active Visual Exploration
Jan Olszewski, Dawid Rymarczyk, Piotr Wójcik +2
Active Visual Exploration (AVE) optimizes the utilization of robotic resources in real-world scenarios by sequentially selecting the most informative observations. However, modern…
Beyond Grids: Exploring Elastic Input Sampling for Vision Transformers
Adam Pardyl, Grzegorz Kurzejamski, Jan Olszewski +2
Vision transformers have excelled in various computer vision tasks but mostly rely on rigid input sampling using a fixed-size grid of patches. It limits their applicability in real…