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
- September 2024: VeLoRA paper got accepted to NeurIPS 2024. Congratulations to the team!
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February 2024: Two papers accepted to CVPR 2024. Congratulations to Prady for G3DR and Roy for VKD! See you at Seattle.
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November 2023: One paper got accepted to AAAI 2024. Congratulations to Chencheng!
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April 2023: Started a new position as a Senior Research Scientist at Huawei Noah's Ark lab, working with Jiankang Deng. Very excited for the new work and for moving to London!
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February 2023: One paper got accepted to CVPR 2023. Congratulations to Jenny!
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September 2022: Two papers accepted to NeurIPS 2022. Congratulations to Vlad and Peter. Happy to be back to the US after covid.
Students and Interns Supervised
- Xin Wen (Intern at Huawei from the University of Hong Kong, 2024)
- Aysim Toker (Intern at Huawei from the Technical University of Munich, 2024)
- Tatiana Gaintseva (Intern at Huawei from Queen Mary University, 2024)
- Changrui Chen (Intern at Huawei from University of Warvick, 2024)
- Bingchen Zhao (Intern at Huawei from University of Edinburgh, 2024)
- Prannay Kaul (Intern at Huawei from University of Oxford, VGG, 2024)→ Applied Scientist at Amazon
- Roy Miles (Intern at Huawei from Imperial College London, 2023) → Research Scientist at Huawei
- Konstantinos Alexandridis (Intern at Huawei from King's College London, 2023) → Research Scientist at Huawei
- Yunqi Miao (Intern at Huawei from Warwick University, 2023) → Research Scientist at Huawei
- Chencheng Ma (Intern at Huawei from Chinese Academy of Sciences, 2023) → Research Scientist at Kunlun
- Volodymyr Fomenko (Master thesis at TUM, 2022) → Technical Staff at OpenAI
- Jenny Seidenschwarz (Master thesis and Ph.D. student at TUM, 2020-2023) → Ph.D. student at TUM
- Franziska Gerken (Ph.D. student at TUM, 2020-2023) → Ph.D. student at TUM
- Peter Kocsis (Master thesis at TUM, 2022) → Ph.D. student at TUM
- Peter Sukenik (Guided Research at TUM, 2021) → Ph.D. student at IST Austriat
- Feliks Hibraj (Research Intern at TUM, 2021) → Computer vision software engineer at SNAP
- Laurin Wagner (Master thesis at TUM, 2020) → Machine learnign software engineer myReha
Service
- Area Chair (AC) for WACV 2021.
- Reviewer for top conferences and journals in Computer Vision and Machine Learning: CVPR, ECCV, ICCV, NeurIPS, ICML, ICLR, IJCV, TMLR, etc. I even managed to get outstanding reviewer awards in CVPR 2021 and ICCV 2021
- I co-organized Deep Visual Similarity and Metric Learning tutorial in CVPR 2022.
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