Search Results for author: Nikos Komodakis

Found 32 papers, 22 papers with code

What to Hide from Your Students: Attention-Guided Masked Image Modeling

no code implementations23 Mar 2022 Ioannis Kakogeorgiou, Spyros Gidaris, Bill Psomas, Yannis Avrithis, Andrei Bursuc, Konstantinos Karantzalos, Nikos Komodakis

Transformers and masked language modeling are quickly being adopted and explored in computer vision as vision transformers and masked image modeling (MIM).

Language Modelling Masked Language Modeling +1

OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning

2 code implementations CVPR 2021 Spyros Gidaris, Andrei Bursuc, Gilles Puy, Nikos Komodakis, Matthieu Cord, Patrick Pérez

With this in mind, we propose a teacher-student scheme to learn representations by training a convolutional net to reconstruct a bag-of-visual-words (BoW) representation of an image, given as input a perturbed version of that same image.

object-detection Object Detection +5

Learning Representations by Predicting Bags of Visual Words

1 code implementation CVPR 2020 Spyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Pérez, Matthieu Cord

Inspired by the success of NLP methods in this area, in this work we propose a self-supervised approach based on spatially dense image descriptions that encode discrete visual concepts, here called visual words.

Representation Learning

QUEST: Quantized embedding space for transferring knowledge

1 code implementation ECCV 2020 Himalaya Jain, Spyros Gidaris, Nikos Komodakis, Patrick Pérez, Matthieu Cord

Knowledge distillation refers to the process of training a compact student network to achieve better accuracy by learning from a high capacity teacher network.

Knowledge Distillation

Deep Tone Mapping Operator for High Dynamic Range Images

no code implementations12 Aug 2019 Aakanksha Rana, Praveer Singh, Giuseppe Valenzise, Frederic Dufaux, Nikos Komodakis, Aljosa Smolic

In this paper, we address this problem by proposing a fast, parameter-free and scene-adaptable deep tone mapping operator (DeepTMO) that yields a high-resolution and high-subjective quality tone mapped output.

Tone Mapping

Boosting Few-Shot Visual Learning with Self-Supervision

1 code implementation ICCV 2019 Spyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Pérez, Matthieu Cord

Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data.

Few-Shot Learning Self-Supervised Learning

Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning

1 code implementation CVPR 2019 Spyros Gidaris, Nikos Komodakis

The meta-model, given as input some novel classes with few training examples per class, must properly adapt the existing recognition model into a new model that can correctly classify in a unified way both the novel and the base classes.

Classification Denoising +2

Exploring Weight Symmetry in Deep Neural Networks

1 code implementation28 Dec 2018 Xu Shell Hu, Sergey Zagoruyko, Nikos Komodakis

We propose several ways to impose local symmetry in recurrent and convolutional neural networks, and show that our symmetry parameterizations satisfy universal approximation property for single hidden layer networks.

Language Modelling

Scattering Networks for Hybrid Representation Learning

1 code implementation17 Sep 2018 Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew Blaschko, Eugene Belilovsky

In particular, by working in scattering space, we achieve competitive results both for supervised and unsupervised learning tasks, while making progress towards constructing more interpretable CNNs.

Representation Learning

Dynamic Few-Shot Visual Learning without Forgetting

4 code implementations CVPR 2018 Spyros Gidaris, Nikos Komodakis

In this context, the goal of our work is to devise a few-shot visual learning system that during test time it will be able to efficiently learn novel categories from only a few training data while at the same time it will not forget the initial categories on which it was trained (here called base categories).

Few-Shot Image Classification General Classification +2

Unsupervised Representation Learning by Predicting Image Rotations

18 code implementations ICLR 2018 Spyros Gidaris, Praveer Singh, Nikos Komodakis

However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale.

General Classification Representation Learning +1

Newton-type Methods for Inference in Higher-Order Markov Random Fields

no code implementations CVPR 2017 Hariprasad Kannan, Nikos Komodakis, Nikos Paragios

Linear programming relaxations are central to {\sc map} inference in discrete Markov Random Fields.

DiracNets: Training Very Deep Neural Networks Without Skip-Connections

3 code implementations1 Jun 2017 Sergey Zagoruyko, Nikos Komodakis

Deep neural networks with skip-connections, such as ResNet, show excellent performance in various image classification benchmarks.

Image Classification

Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling

1 code implementation CVPR 2017 Spyros Gidaris, Nikos Komodakis

Instead, we propose a generic architecture that decomposes the label improvement task to three steps: 1) detecting the initial label estimates that are incorrect, 2) replacing the incorrect labels with new ones, and finally 3) refining the renewed labels by predicting residual corrections w. r. t.

Disparity Estimation Stereo Matching +2

A Deep Metric for Multimodal Registration

no code implementations17 Sep 2016 Martin Simonovsky, Benjamín Gutiérrez-Becker, Diana Mateus, Nassir Navab, Nikos Komodakis

Multimodal registration is a challenging problem in medical imaging due the high variability of tissue appearance under different imaging modalities.

OnionNet: Sharing Features in Cascaded Deep Classifiers

no code implementations9 Aug 2016 Martin Simonovsky, Nikos Komodakis

The focus of our work is speeding up evaluation of deep neural networks in retrieval scenarios, where conventional architectures may spend too much time on negative examples.

Image Retrieval object-detection +2

Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

1 code implementation14 Jun 2016 Spyros Gidaris, Nikos Komodakis

We extensively evaluate our AttractioNet approach on several image datasets (i. e. COCO, PASCAL, ImageNet detection and NYU-Depth V2 datasets) reporting on all of them state-of-the-art results that surpass the previous work in the field by a significant margin and also providing strong empirical evidence that our approach is capable to generalize to unseen categories.

object-detection Object Detection

Wide Residual Networks

67 code implementations23 May 2016 Sergey Zagoruyko, Nikos Komodakis

Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance.

Image Classification

HARF: Hierarchy-Associated Rich Features for Salient Object Detection

no code implementations ICCV 2015 Wenbin Zou, Nikos Komodakis

This leads to a rich feature representation, which is able to represent the context of the whole object/background and is much more discriminative as well as robust for salient object detection.

object-detection RGB Salient Object Detection +2

Object Detection via a Multi-Region and Semantic Segmentation-Aware CNN Model

1 code implementation ICCV 2015 Spyros Gidaris, Nikos Komodakis

We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features.

object-detection Object Detection +2

LocNet: Improving Localization Accuracy for Object Detection

1 code implementation CVPR 2016 Spyros Gidaris, Nikos Komodakis

We propose a novel object localization methodology with the purpose of boosting the localization accuracy of state-of-the-art object detection systems.

object-detection Object Detection +1

Object detection via a multi-region & semantic segmentation-aware CNN model

1 code implementation7 May 2015 Spyros Gidaris, Nikos Komodakis

We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features.

object-detection Object Detection +2

Learning to Compare Image Patches via Convolutional Neural Networks

1 code implementation CVPR 2015 Sergey Zagoruyko, Nikos Komodakis

In this paper we show how to learn directly from image data (i. e., without resorting to manually-designed features) a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems.

Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers

no code implementations NeurIPS 2014 Bruno Conejo, Nikos Komodakis, Sebastien Leprince, Jean Philippe Avouac

We propose a general and versatile framework that significantly speeds-up graphical model optimization while maintaining an excellent solution accuracy.

Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems

no code implementations20 Jun 2014 Nikos Komodakis, Jean-Christophe Pesquet

Optimization methods are at the core of many problems in signal/image processing, computer vision, and machine learning.

Learning to Detect Ground Control Points for Improving the Accuracy of Stereo Matching

no code implementations CVPR 2014 Aristotle Spyropoulos, Nikos Komodakis, Philippos Mordohai

While machine learning has been instrumental to the ongoing progress in most areas of computer vision, it has not been applied to the problem of stereo matching with similar frequency or success.

Stereo Matching Stereo Matching Hand

A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems

no code implementations2 Apr 2014 Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, Carsten Rother

However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.

Clustering via LP-based Stabilities

1 code implementation NeurIPS 2008 Nikos Komodakis, Nikos Paragios, Georgios Tziritas

To deal with the most critical issue in a center-based clustering algorithm (selection of cluster centers), we also introduce the notion of stability of a cluster center, which is a well defined LP-based quantity that plays a key role to our algorithm's success.

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