209 citations · 374 across the 12 of their papers we have counts for
6 papers · 1 filter
DeepScores and Deep Watershed Detection: current state and open issues
Ismail Elezi, Lukas Tuggener, Marcello Pelillo +1
This paper gives an overview of our current Optical Music Recognition (OMR) research. We recently released the OMR dataset \emph{DeepScores} as well as the object detection method…
Deep Learning in the Wild
Thilo Stadelmann, Mohammadreza Amirian, Ismail Arabaci +8
Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range…
Learning Neural Models for End-to-End Clustering
Benjamin Bruno Meier, Ismail Elezi, Mohammadreza Amirian +2
We propose a novel end-to-end neural network architecture that, once trained, directly outputs a probabilistic clustering of a batch of input examples in one pass. It estimates a d…
Deep Watershed Detector for Music Object Recognition
Lukas Tuggener, Ismail Elezi, Jurgen Schmidhuber +1
Optical Music Recognition (OMR) is an important and challenging area within music information retrieval, the accurate detection of music symbols in digital images is a core functio…
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects
Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber +2
We present the DeepScores dataset with the goal of advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of…
Speaker Clustering Using Dominant Sets
Feliks Hibraj, Sebastiano Vascon, Thilo Stadelmann +1
Speaker clustering is the task of forming speaker-specific groups based on a set of utterances. In this paper, we address this task by using Dominant Sets (DS). DS is a graph-based…