Ismail Elezi, Ph.D.

Hi, I'm Ismail. I am a Senior Research Scientist at Huawei Noah's Ark in London, working with Jiankang Deng, where I am leading one of the multi-modality learning teams.

After finishing my bachelor studies at the University of Prishtina, Kosovo, I obtained both my master's and Ph.D. degree at Ca' Foscari University of Venice, Italy, advised by professors Marcello Pelillo and Thilo Stadelmann. Then, I worked as a postdoctoral researcher with professor Laura Leal-Taixé at Technical University of Munich and interned with Jose M. Alvarez at NVIDIA, Santa Clara.


If you have a first author paper in a top ML/CV conference, and want to do an internship with me, please send me an email (find it in my CV). My interns/mentees usually author papers with me and go on to great companies or PhD programs.

Twitter Scholar Linkedin GitHub CV

News

Students and Interns Supervised

Service

Teaching

Selected Publications

P. Reddy*, I. Elezi*, J. Deng: G3DR: Generative 3D Reconstruction in ImageNet, Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
page code paper


R. Miles, I. Elezi, J. Deng: VkD: Improving Knowledge Distillation using Orthogonal Projections, Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
code paper


C. Ma, I. Elezi, J. Deng, W. Dong, C. Xu: Three Heads Are Better Than One: Complementary Experts for Long-Tailed Semi-supervised Learning, Conference on Artificial Intelligence (AAAI), 2024.
code paper


J. Seidenschwarz, G. Braso, V. Serrano, I. Elezi, L. Leal-Taixé: Simple Cues Lead to a Strong Multi-Object Tracker, Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
code paper video


I. Elezi*, J. Seidenschwarz*, L. Wagner*, S. Vascon, A. Torcinovich, M. Pelillo, L. Leal-Taixé: The group loss++: A deeper look into group loss for deep metric learning, Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2023.
paper


V. Fomenko, I. Elezi, D. Ramanan, L. Leal-Taixé, A. Ošep: Learning to Discover and Detect Objects, Neural Information Processing Systems (NeurIPS), 2022.
page poster video code paper


P. Kocsis, P. Sukenik, G. Braso, M. Niessner, L. Leal-Taixé, I. Elezi: The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes, Neural Information Processing Systems (NeurIPS), 2022.
page code paper


I. Elezi, Z. Yu, A. Anandkumar, L. Leal-Taixé, J. Alvarez: Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection, Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
code paper


J. Choi, I. Elezi, H. Lee, C. Farabet, J. Alvarez: Active Learning for Deep Object Detection via Probabilistic Modeling, International Conference on Computer Vision (ICCV), 2021.
code paper video


J. Seidenschwarz, I. Elezi, L. Leal-Taixé: Learning Intra-Batch Connections for Deep Metric Learning, International Conference on Machine Learning (ICML), 2021.
code paper video


I. Elezi, S. Vascon, A. Torcinovich, M. Pelillo, L. Leal-Taixé: The Group Loss for Deep Metric Learning, European Conference on Computer Vision (ECCV), 2020.
page code paper video


M. Maximov*, I. Elezi*, L. Leal-Taixé: CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks, Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
code paper video