Ismail Elezi, Ph.D.

Hi, I'm Ismail. I am a Principal 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.

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News

• February 2025: Got a paper accepted to CVPR 2025. Congratulations to Kostas.
• January 2025: Finally got a paper accepted to ICLR. Congratulations to my former intern Prannay and see you in Singapore
• September 2024: One paper accepted to NeurIPS 2024. Congratulations to Roy for VeLoRA. See you in Vancouver.
• February 2024: Two papers accepted to CVPR 2024. Congratulations to Prady for G3DR and Roy for VKD! See you at Seattle.
• November 2023: One paper got accepted to AAAI 2024. Congratulations to Chencheng!
• 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!
• February 2023: One paper got accepted to CVPR 2023. Congratulations to Jenny!
• 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

• Yura Choi (Yonsei University, 2025)
• Ye-Bin Moon (POSTECH, 2025)
• Xin Wen (University of Hong Kong, 2024-2025)
• Aysim Toker (Technical Unversity of Munich, 2024-2025)
• Tatiana Gaintseva (Queen Mary University, 2024-2025)
• Bingchen Zhao (University of Edinburgh, 2024)
• Changrui Chen (Warvick University, 2024) → Research Scientist at Huawei
• Prannay Kaul (University of Oxford, VGG, 2024)→ Applied Scientist at Amazon
• Roy Miles (Imperial College London, 2023) → Research Scientist at Huawei
• Konstantinos Alexandridis (King's College London, 2023) → Research Scientist at Huawei
• Yunqi Miao (Warwick University, 2023) → Research Scientist at Huawei
• Chencheng Ma (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

• Summer 2022: Lecturer for IN2375: Computer Vision III: Detection, Segmentation and Tracking (CV3DST) (TU Munich)
• Summer 2022: Lecturer for IN2346: Introduction to Deep Learning (I2DL) (TU Munich)
• Winter 2021: Lecturer for IIN2375: Computer Vision III: Detection, Segmentation and Tracking (CV3DST) (TU Munich)
• Winter 2021: Lecturer for IN2389: Advanced Deep Learning for Computer vision (ADL4CV) (TU Munich)
• 2019-2023: Lecturer for Introduction to Deep Learning with PyTorch (Datacamp). Around 30K students took the course before it got retired in 2023.
• I have a few lectures online in Transformers, Semi-Supervised Learning, and Active Learning. They are a couple of years old so relatively outdated.

Selected Publications

K. Alexandridis, I. Elezi, J. Deng, A. Nguyen, S. Luo: Fractal Calibration for long-tailed object detection, Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
paper

P. Kaul, C. Ma, I. Elezi, J. Deng: From Attention to Activation: Unraveling the Enigmas of Large Language Models, International Conference on Learning Representations (ICLR), 2025.
paper

R. Miles, P.Reddy, I. Elezi, J. Deng: VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections, Neural Information Processing Systems (NeurIPS), 2024.
code paper

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