Search Results for author: Spyros Gidaris

Found 27 papers, 24 papers with code

Valeo4Cast: A Modular Approach to End-to-End Forecasting

no code implementations12 Jun 2024 Yihong Xu, Éloi Zablocki, Alexandre Boulch, Gilles Puy, Mickael Chen, Florent Bartoccioni, Nermin Samet, Oriane Siméoni, Spyros Gidaris, Tuan-Hung Vu, Andrei Bursuc, Eduardo Valle, Renaud Marlet, Matthieu Cord

Motion forecasting is crucial in autonomous driving systems to anticipate the future trajectories of surrounding agents such as pedestrians, vehicles, and traffic signals.

Motion Forecasting

POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images

no code implementations NeurIPS 2023 Antonin Vobecky, Oriane Siméoni, David Hurych, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Josef Sivic

We describe an approach to predict open-vocabulary 3D semantic voxel occupancy map from input 2D images with the objective of enabling 3D grounding, segmentation and retrieval of free-form language queries.

3D Semantic Occupancy Prediction 3D Semantic Segmentation +3

Three Pillars improving Vision Foundation Model Distillation for Lidar

1 code implementation CVPR 2024 Gilles Puy, Spyros Gidaris, Alexandre Boulch, Oriane Siméoni, Corentin Sautier, Patrick Pérez, Andrei Bursuc, Renaud Marlet

In particular, thanks to our scalable distillation method named ScaLR, we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality.

Autonomous Driving Object Discovery +2

Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey

1 code implementation19 Oct 2023 Oriane Siméoni, Éloi Zablocki, Spyros Gidaris, Gilles Puy, Patrick Pérez

We propose here a survey of unsupervised object localization methods that discover objects in images without requiring any manual annotation in the era of self-supervised ViTs.

Object Unsupervised Object Localization

RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving

1 code implementation CVPR 2023 Angelika Ando, Spyros Gidaris, Andrei Bursuc, Gilles Puy, Alexandre Boulch, Renaud Marlet

(c) We refine pixel-wise predictions with a convolutional decoder and a skip connection from the convolutional stem to combine low-level but fine-grained features of the the convolutional stem with the high-level but coarse predictions of the ViT encoder.

3D Semantic Segmentation Autonomous Driving +1

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 +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.

Image Segmentation Segmentation +1

Localizing Objects with Self-Supervised Transformers and no Labels

2 code implementations29 Sep 2021 Oriane Siméoni, Gilles Puy, Huy V. Vo, Simon Roburin, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Renaud Marlet, Jean Ponce

We also show that training a class-agnostic detector on the discovered objects boosts results by another 7 points.

Ranked #4 on Weakly-Supervised Object Localization on CUB-200-2011 (Top-1 Localization Accuracy metric)

Object Object Discovery +2

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 +3

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

20 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 object-detection +3

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 object-detection +2

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 object-detection +4

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