Ismail Elezi | DeepAI

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∙ 06/20/2023

Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis

The advent of data-driven technology solutions is accompanied by an incr...

9 Maxim Maximov, et al. ∙

∙ 10/19/2022

Learning to Discover and Detect Objects

We tackle the problem of novel class discovery, detection, and localizat...

0 Vladimir Fomenko, et al. ∙

∙ 10/11/2022

The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes

Convolutional neural networks were the standard for solving many compute...

0 Peter Kocsis, et al. ∙

∙ 06/09/2022

Simple Cues Lead to a Strong Multi-Object Tracker

For a long time, the most common paradigm in Multi-Object Tracking was t...

0 Jenny Seidenschwarz, et al. ∙

∙ 06/22/2021

Towards Reducing Labeling Cost in Deep Object Detection

Deep neural networks have reached very high accuracy on object detection...

7 Ismail Elezi, et al. ∙

∙ 06/17/2021

The 2021 Image Similarity Dataset and Challenge

This paper introduces a new benchmark for large-scale image similarity d...

23 Matthijs Douze, et al. ∙

∙ 03/30/2021

Active Learning for Deep Object Detection via Probabilistic Modeling

Active learning aims to reduce labeling costs by selecting only the most...

0 Jiwoong Choi, et al. ∙

∙ 02/15/2021

Learning Intra-Batch Connections for Deep Metric Learning

The goal of metric learning is to learn a function that maps samples to ...

14 Jenny Seidenschwarz, et al. ∙

∙ 06/21/2020

Exploiting Contextual Information with Deep Neural Networks

Context matters! Nevertheless, there has not been much research in explo...

0 Ismail Elezi, et al. ∙

∙ 05/19/2020

CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks

The unprecedented increase in the usage of computer vision technology in...

15 Maxim Maximov, et al. ∙

∙ 12/01/2019

The Group Loss for Deep Metric Learning

Deep metric learning has yielded impressive results in tasks such as clu...

46 Ismail Elezi, et al. ∙

∙ 10/12/2018

DeepScores and Deep Watershed Detection: current state and open issues

This paper gives an overview of our current Optical Music Recognition (O...

0 Ismail Elezi, et al. ∙

∙ 07/13/2018

Deep Learning in the Wild

Deep learning with neural networks is applied by an increasing number of...

4 Thilo Stadelmann, et al. ∙

∙ 07/11/2018

Learning Neural Models for End-to-End Clustering

We propose a novel end-to-end neural network architecture that, once tra...

0 Benjamin Bruno Meier, et al. ∙

∙ 05/26/2018

Deep Watershed Detector for Music Object Recognition

Optical Music Recognition (OMR) is an important and challenging area wit...

0 Lukas Tuggener, et al. ∙

∙ 05/26/2018

Transductive Label Augmentation for Improved Deep Network Learning

A major impediment to the application of deep learning to real-world pro...

0 Ismail Elezi, et al. ∙

∙ 03/27/2018

DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects

We present the DeepScores dataset with the goal of advancing the state-o...

0 Lukas Tuggener, et al. ∙

∙ 09/21/2016

Revealing Structure in Large Graphs: Szemerédi's Regularity Lemma and its Use in Pattern Recognition

Introduced in the mid-1970's as an intermediate step in proving a long-s...

0 Marcello Pelillo, et al. ∙

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