no code implementations • 21 Mar 2024 • Edrina Gashi, Jiankang Deng, Ismail Elezi
By uncovering the limitations of current methods and understanding the impact of different experimental settings, we aim to inspire more efficient training of deep learning models in real-world scenarios with limited annotation budgets.
1 code implementation • 10 Mar 2024 • Roy Miles, Ismail Elezi, Jiankang Deng
Knowledge distillation is an effective method for training small and efficient deep learning models.
Ranked #2 on
Knowledge Distillation
on ImageNet
1 code implementation • 1 Mar 2024 • Pradyumna Reddy, Ismail Elezi, Jiankang Deng
We introduce a novel 3D generative method, Generative 3D Reconstruction (G3DR) in ImageNet, capable of generating diverse and high-quality 3D objects from single images, addressing the limitations of existing methods.
1 code implementation • 25 Dec 2023 • Chengcheng Ma, Ismail Elezi, Jiankang Deng, WeiMing Dong, Changsheng Xu
For instance, on CIFAR-10-LT, CPE improves test accuracy by over 2. 22% compared to baselines.
no code implementations • 20 Jun 2023 • Maxim Maximov, Tim Meinhardt, Ismail Elezi, Zoe Papakipos, Caner Hazirbas, Cristian Canton Ferrer, Laura Leal-Taixé
To highlight the importance of privacy issues and motivate future research, we motivate and introduce the Pedestrian Dataset De-Identification (PDI) task.
1 code implementation • 19 Oct 2022 • Vladimir Fomenko, Ismail Elezi, Deva Ramanan, Laura Leal-Taixé, Aljoša Ošep
We then train our network to learn to classify each RoI, either as one of the known classes, seen in the source dataset, or one of the novel classes, with a long-tail distribution constraint on the class assignments, reflecting the natural frequency of classes in the real world.
Ranked #2 on
Novel Object Detection
on LVIS v1.0 val
no code implementations • 11 Oct 2022 • Peter Kocsis, Peter Súkeník, Guillem Brasó, Matthias Nießner, Laura Leal-Taixé, Ismail Elezi
This allows us to improve the generalization of a CNN-based model without any increase in the number of weights at test time.
1 code implementation • CVPR 2023 • Jenny Seidenschwarz, Guillem Brasó, Victor Castro Serrano, Ismail Elezi, Laura Leal-Taixé
For association, most models resourced to motion and appearance cues, e. g., re-identification networks.
no code implementations • 4 Apr 2022 • Ismail Elezi, Jenny Seidenschwarz, Laurin Wagner, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixe
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes.
no code implementations • CVPR 2022 • Ismail Elezi, Zhiding Yu, Anima Anandkumar, Laura Leal-Taixe, Jose M. Alvarez
Deep neural networks have reached high accuracy on object detection but their success hinges on large amounts of labeled data.
1 code implementation • 17 Jun 2021 • Matthijs Douze, Giorgos Tolias, Ed Pizzi, Zoë Papakipos, Lowik Chanussot, Filip Radenovic, Tomas Jenicek, Maxim Maximov, Laura Leal-Taixé, Ismail Elezi, Ondřej Chum, Cristian Canton Ferrer
This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021).
Ranked #1 on
Image Similarity Detection
on DISC21 dev
1 code implementation • ICCV 2021 • Jiwoong Choi, Ismail Elezi, Hyuk-Jae Lee, Clement Farabet, Jose M. Alvarez
Most of these methods are based on multiple models or are straightforward extensions of classification methods, hence estimate an image's informativeness using only the classification head.
2 code implementations • 15 Feb 2021 • Jenny Seidenschwarz, Ismail Elezi, Laura Leal-Taixé
To this end, we propose an approach based on message passing networks that takes all the relations in a mini-batch into account.
Ranked #3 on
Metric Learning
on CARS196
no code implementations • 1 Jan 2021 • Jiwoong Choi, Ismail Elezi, Hyuk-Jae Lee, Clement Farabet, Jose M. Alvarez
For active learning, we propose a scoring function that aggregates uncertainties from both the classification and the localization outputs of the network.
no code implementations • 21 Jun 2020 • Ismail Elezi
In this thesis, we show that contextual information can be exploited in 2 fundamentally different ways: implicitly and explicitly.
1 code implementation • CVPR 2020 • Maxim Maximov, Ismail Elezi, Laura Leal-Taixé
In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity.
2 code implementations • ECCV 2020 • Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixe
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes.
Ranked #20 on
Metric Learning
on CUB-200-2011
(using extra training data)
1 code implementation • 12 Oct 2018 • Ismail Elezi, Lukas Tuggener, Marcello Pelillo, Thilo Stadelmann
This paper gives an overview of our current Optical Music Recognition (OMR) research.
no code implementations • 13 Jul 2018 • 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 with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks.
1 code implementation • 11 Jul 2018 • Benjamin Bruno Meier, Ismail Elezi, Mohammadreza Amirian, Oliver Durr, Thilo Stadelmann
We propose a novel end-to-end neural network architecture that, once trained, directly outputs a probabilistic clustering of a batch of input examples in one pass.
no code implementations • 26 May 2018 • Ismail Elezi, Alessandro Torcinovich, Sebastiano Vascon, Marcello Pelillo
Motivated by the observation that in virtually all application domains it is easy to obtain unlabeled data, in this paper we take a different perspective and propose a \emph{label augmentation} approach.
no code implementations • 26 May 2018 • Lukas Tuggener, Ismail Elezi, Jurgen Schmidhuber, Thilo Stadelmann
Optical Music Recognition (OMR) is an important and challenging area within music information retrieval, the accurate detection of music symbols in digital images is a core functionality of any OMR pipeline.
2 code implementations • 27 Mar 2018 • Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Marcello Pelillo, Thilo Stadelmann
We present the DeepScores dataset with the goal of advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of scene understanding.
no code implementations • 21 Sep 2016 • Marcello Pelillo, Ismail Elezi, Marco Fiorucci
Introduced in the mid-1970's as an intermediate step in proving a long-standing conjecture on arithmetic progressions, Szemer\'edi's regularity lemma has emerged over time as a fundamental tool in different branches of graph theory, combinatorics and theoretical computer science.