5 papers · 1 filter
Learning from Random Subspace Exploration: Generalized Test-Time Augmentation with Self-supervised Distillation
Andrei Jelea, Ahmed Nabil Belbachir, Marius Leordeanu
We introduce Generalized Test-Time Augmentation (GTTA), a highly effective method for improving the performance of a trained model, which unlike other existing Test-Time Augmentati…
Closer to Ground Truth: Realistic Shape and Appearance Labeled Data Generation for Unsupervised Underwater Image Segmentation
Andrei Jelea, Ahmed Nabil Belbachir, Marius Leordeanu
Solving fish segmentation in underwater videos, a real-world problem of great practical value in marine and aquaculture industry, is a challenging task due to the difficulty of the…
Precision in Building Extraction: Comparing Shallow and Deep Models using LiDAR Data
Muhammad Sulaiman, Mina Farmanbar, Ahmed Nabil Belbachir +1
Building segmentation is essential in infrastructure development, population management, and geological observations. This article targets shallow models due to their interpretable…
Self-supervised Hypergraphs for Learning Multiple World Interpretations
Alina Marcu, Mihai Pirvu, Dragos Costea +5
We present a method for learning multiple scene representations given a small labeled set, by exploiting the relationships between such representations in the form of a multi-task…
Saliency Can Be All You Need In Contrastive Self-Supervised Learning
Veysel Kocaman, Ofer M. Shir, Thomas Bäck +1
We propose an augmentation policy for Contrastive Self-Supervised Learning (SSL) in the form of an already established Salient Image Segmentation technique entitled Global Contrast…