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ORCIDIsmail Elezi
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2020 – today
- 2024
[c17]Chengcheng Ma, Ismail Elezi, Jiankang Deng, Weiming Dong, Changsheng Xu:
Three Heads Are Better than One: Complementary Experts for Long-Tailed Semi-supervised Learning. AAAI 2024: 14229-14237- [c16]Pradyumna Reddy, Ismail Elezi, Jiankang Deng:
G3DR: Generative 3D Reconstruction in ImageNet. CVPR 2024: 9655-9665 - [c15]Roy Miles, Ismail Elezi, Jiankang Deng:
$V_{k}D$: Improving Knowledge Distillation Using Orthogonal Projections. CVPR 2024: 15720-15730 - [i24]Pradyumna Reddy, Ismail Elezi, Jiankang Deng:
G3DR: Generative 3D Reconstruction in ImageNet. CoRR abs/2403.00939 (2024) - [i23]Roy Miles, Ismail Elezi, Jiankang Deng:
VkD: Improving Knowledge Distillation using Orthogonal Projections. CoRR abs/2403.06213 (2024) - [i22]Edrina Gashi, Jiankang Deng, Ismail Elezi:
Deep Active Learning: A Reality Check. CoRR abs/2403.14800 (2024) - [i21]Roy Miles, Pradyumna Reddy, Ismail Elezi, Jiankang Deng:
VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections. CoRR abs/2405.17991 (2024) - 2023
- [j2]Ismail Elezi
, Jenny Seidenschwarz, Laurin Wagner, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixé:
The Group Loss++: A Deeper Look Into Group Loss for Deep Metric Learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(2): 2505-2518 (2023) - [c14]Jenny Seidenschwarz, Guillem Brasó, Victor Castro Serrano, Ismail Elezi, Laura Leal-Taixé:
Simple Cues Lead to a Strong Multi-Object Tracker. CVPR 2023: 13813-13823 - [i20]Maxim Maximov, Tim Meinhardt, Ismail Elezi, Zoe Papakipos, Caner Hazirbas, Cristian Canton-Ferrer, Laura Leal-Taixé:
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis. CoRR abs/2306.11710 (2023) - [i19]Chengcheng Ma, Ismail Elezi, Jiankang Deng, Weiming Dong, Changsheng Xu:
Three Heads Are Better Than One: Complementary Experts for Long-Tailed Semi-supervised Learning. CoRR abs/2312.15702 (2023) - 2022
- [c13]Maxim Maximov, Ismail Elezi, Laura Leal-Taixé:
Decoupling Identity and Visual Quality for Image and Video Anonymization. ACCV (6) 2022: 510-526 - [c12]Ismail Elezi, Zhiding Yu, Anima Anandkumar, Laura Leal-Taixé, José M. Álvarez:
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection. CVPR 2022: 14472-14481 - [c11]Peter Kocsis, Peter Súkeník, Guillem Brasó, Matthias Nießner, Laura Leal-Taixé, Ismail Elezi:
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes. NeurIPS 2022 - [c10]Vladimir Fomenko, Ismail Elezi, Deva Ramanan, Laura Leal-Taixé, Aljosa Osep:
Learning to Discover and Detect Objects. NeurIPS 2022 - [i18]Ismail Elezi, Jenny Seidenschwarz, Laurin Wagner, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixé:
The Group Loss++: A deeper look into group loss for deep metric learning. CoRR abs/2204.01509 (2022) - [i17]Jenny Seidenschwarz, Guillem Brasó, Ismail Elezi, Laura Leal-Taixé:
Simple Cues Lead to a Strong Multi-Object Tracker. CoRR abs/2206.04656 (2022) - [i16]Peter Kocsis, Peter Súkeník, Guillem Brasó, Matthias Nießner, Laura Leal-Taixé, Ismail Elezi:
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes. CoRR abs/2210.05657 (2022) - [i15]Vladimir Fomenko, Ismail Elezi, Deva Ramanan, Laura Leal-Taixé, Aljosa Osep:
Learning to Discover and Detect Objects. CoRR abs/2210.10774 (2022) - 2021
- [c9]Jiwoong Choi, Ismail Elezi, Hyuk-Jae Lee, Clément Farabet, José M. Álvarez:
Active Learning for Deep Object Detection via Probabilistic Modeling. ICCV 2021: 10244-10253 - [c8]Jenny Denise Seidenschwarz, Ismail Elezi, Laura Leal-Taixé:
Learning Intra-Batch Connections for Deep Metric Learning. ICML 2021: 9410-9421 - [i14]Jenny Seidenschwarz, Ismail Elezi, Laura Leal-Taixé:
Learning Intra-Batch Connections for Deep Metric Learning. CoRR abs/2102.07753 (2021) - [i13]Jiwoong Choi, Ismail Elezi, Hyuk-Jae Lee, Clément Farabet, José M. Álvarez:
Active Learning for Deep Object Detection via Probabilistic Modeling. CoRR abs/2103.16130 (2021) - [i12]Matthijs Douze, Giorgos Tolias, Ed Pizzi, Zoë Papakipos, Lowik Chanussot, Filip Radenovic, Tomás Jenícek, Maxim Maximov, Laura Leal-Taixé, Ismail Elezi, Ondrej Chum, Cristian Canton-Ferrer:
The 2021 Image Similarity Dataset and Challenge. CoRR abs/2106.09672 (2021) - [i11]Ismail Elezi, Zhiding Yu, Anima Anandkumar, Laura Leal-Taixé, José M. Álvarez:
Towards Reducing Labeling Cost in Deep Object Detection. CoRR abs/2106.11921 (2021) - 2020
- [c7]Maxim Maximov, Ismail Elezi, Laura Leal-Taixé:
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks. CVPR 2020: 5446-5455 - [c6]Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixé:
The Group Loss for Deep Metric Learning. ECCV (7) 2020: 277-294 - [i10]Maxim Maximov, Ismail Elezi, Laura Leal-Taixé:
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks. CoRR abs/2005.09544 (2020) - [i9]Ismail Elezi:
Exploiting Contextual Information with Deep Neural Networks. CoRR abs/2006.11706 (2020)
2010 – 2019
- 2019
- [i8]Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixé:
The Group Loss for Deep Metric Learning. CoRR abs/1912.00385 (2019) - 2018
- [c5]Thilo Stadelmann, Mohammadreza Amirian, Ismail Arabaci, Marek Arnold, Gilbert François Duivesteijn, Ismail Elezi, Melanie Geiger, Stefan Lörwald, Benjamin Bruno Meier, Katharina Rombach, Lukas Tuggener:
Deep Learning in the Wild. ANNPR 2018: 17-38 - [c4]Benjamin Bruno Meier, Ismail Elezi, Mohammadreza Amirian, Oliver Dürr, Thilo Stadelmann:
Learning Neural Models for End-to-End Clustering. ANNPR 2018: 126-138 - [c3]Ismail Elezi, Alessandro Torcinovich, Sebastiano Vascon, Marcello Pelillo:
Transductive Label Augmentation for Improved Deep Network Learning. ICPR 2018: 1432-1437 - [c2]Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Marcello Pelillo, Thilo Stadelmann:
DeepScores-A Dataset for Segmentation, Detection and Classification of Tiny Objects. ICPR 2018: 3704-3709 - [c1]Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Thilo Stadelmann:
Deep Watershed Detector for Music Object Recognition. ISMIR 2018: 271-278 - [i7]Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Marcello Pelillo, Thilo Stadelmann:
DeepScores - A Dataset for Segmentation, Detection and Classification of Tiny Objects. CoRR abs/1804.00525 (2018) - [i6]Ismail Elezi, Alessandro Torcinovich, Sebastiano Vascon, Marcello Pelillo:
Transductive Label Augmentation for Improved Deep Network Learning. CoRR abs/1805.10546 (2018) - [i5]Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Thilo Stadelmann:
Deep Watershed Detector for Music Object Recognition. CoRR abs/1805.10548 (2018) - [i4]Benjamin Bruno Meier, Ismail Elezi, Mohammadreza Amirian, Oliver Durr, Thilo Stadelmann:
Learning Neural Models for End-to-End Clustering. CoRR abs/1807.04001 (2018) - [i3]Thilo Stadelmann, Mohammadreza Amirian, Ismail Arabaci, Marek Arnold, Gilbert François Duivesteijn, Ismail Elezi, Melanie Geiger, Stefan Lörwald, Benjamin Bruno Meier, Katharina Rombach, Lukas Tuggener:
Deep Learning in the Wild. CoRR abs/1807.04950 (2018) - [i2]Ismail Elezi, Lukas Tuggener, Marcello Pelillo, Thilo Stadelmann:
DeepScores and Deep Watershed Detection: current state and open issues. CoRR abs/1810.05423 (2018) - 2017
- [j1]Marcello Pelillo, Ismail Elezi, Marco Fiorucci:
Revealing structure in large graphs: Szemerédi's regularity lemma and its use in pattern recognition. Pattern Recognit. Lett. 87: 4-11 (2017) - 2016
- [i1]Marcello Pelillo, Ismail Elezi, Marco Fiorucci:
Revealing Structure in Large Graphs: Szemerédi's Regularity Lemma and its Use in Pattern Recognition. CoRR abs/1609.06583 (2016)
Coauthor Index
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last updated on 2024-10-07 02:27 CEST by the dblp team
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