Search Results for author: Spyros Gidaris

Found 19 papers, 19 papers with code

Active Learning Strategies for Weakly-supervised Object Detection

1 code implementation25 Jul 2022 Huy V. Vo, Oriane Siméoni, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Jean Ponce

On COCO, using on average 10 fully-annotated images per class, or equivalently 1% of the training set, BiB also reduces the performance gap (in AP) between the weakly-supervised detector and the fully-supervised Fast RCNN by over 70%, showing a good trade-off between performance and data efficiency.

Active Learning object-detection +1

Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation

1 code implementation21 Mar 2022 Antonin Vobecky, David Hurych, Oriane Siméoni, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Josef Sivic

This work investigates learning pixel-wise semantic image segmentation in urban scenes without any manual annotation, just from the raw non-curated data collected by cars which, equipped with cameras and LiDAR sensors, drive around a city.

Unsupervised Semantic Segmentation

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

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

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 +3

Unsupervised Representation Learning by Predicting Image Rotations

19 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

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

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

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

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