Spyros Gidaris

papers

876

total citations

papers (17)

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

CVPR 2023arXiv

118

citations

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

CVPR 2021arXiv

citations

EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling

ICML 2025arXiv

citations

SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers

CVPR 2024arXiv

citations

Three Pillars Improving Vision Foundation Model Distillation for Lidar

CVPR 2024arXiv

citations

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

ECCV 2022arXiv

citations

Active Learning Strategies for Weakly-Supervised Object Detection

ECCV 2022arXiv

citations

DINO-Foresight: Looking into the Future with DINO

NEURIPS 2025arXiv

citations

Boosting Generative Image Modeling via Joint Image-Feature Synthesis

NEURIPS 2025arXiv

citations

QuEST: Quantized Embedding Space for Transferring Knowledge

ECCV 2020arXiv

citations

MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments

ICLR 2025arXiv

citations

Advancing Semantic Future Prediction through Multimodal Visual Sequence Transformers

CVPR 2025arXiv

citations

Multi-Token Prediction Needs Registers

NEURIPS 2025arXiv

citations

DIP: Unsupervised Dense In-Context Post-training of Visual Representations

ICCV 2025arXiv

citations

Spyros Gidaris

papers (17)

Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data

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

Learning Representations by Predicting Bags of Visual Words

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

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

EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling

SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers

Three Pillars Improving Vision Foundation Model Distillation for Lidar

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

Active Learning Strategies for Weakly-Supervised Object Detection

DINO-Foresight: Looking into the Future with DINO

Boosting Generative Image Modeling via Joint Image-Feature Synthesis

QuEST: Quantized Embedding Space for Transferring Knowledge

MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments

Advancing Semantic Future Prediction through Multimodal Visual Sequence Transformers

Multi-Token Prediction Needs Registers

DIP: Unsupervised Dense In-Context Post-training of Visual Representations

papers (17)

Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data

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

Learning Representations by Predicting Bags of Visual Words

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

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

EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling

SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers

Three Pillars Improving Vision Foundation Model Distillation for Lidar

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

Active Learning Strategies for Weakly-Supervised Object Detection

DINO-Foresight: Looking into the Future with DINO

Boosting Generative Image Modeling via Joint Image-Feature Synthesis

QuEST: Quantized Embedding Space for Transferring Knowledge

MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments

Advancing Semantic Future Prediction through Multimodal Visual Sequence Transformers

Multi-Token Prediction Needs Registers

DIP: Unsupervised Dense In-Context Post-training of Visual Representations