5 papers
DIP: Unsupervised Dense In-Context Post-training of Visual Representations
Sophia Sirko-Galouchenko, Spyros Gidaris, Antonin Vobecky +2
We introduce DIP, a novel unsupervised post-training method designed to enhance dense image representations in large-scale pretrained vision encoders for in-context scene understan…
Test-time Contrastive Concepts for Open-world Semantic Segmentation with Vision-Language Models
Monika WysoczaÅska, Antonin Vobecky, Amaia Cardiel +4
Recent CLIP-like Vision-Language Models (VLMs), pre-trained on large amounts of image-text pairs to align both modalities with a simple contrastive objective, have paved the way to…
MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments
Spyros Gidaris, Andrei Bursuc, Oriane Simeoni +4
Self-supervised learning can be used for mitigating the greedy needs of Vision Transformer networks for very large fully-annotated datasets. Different classes of self-supervised le…
OccFeat: Self-supervised Occupancy Feature Prediction for Pretraining BEV Segmentation Networks
Sophia Sirko-Galouchenko, Alexandre Boulch, Spyros Gidaris +4
We introduce a self-supervised pretraining method, called OccFeat, for camera-only Bird's-Eye-View (BEV) segmentation networks. With OccFeat, we pretrain a BEV network via occupanc…
Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation
Antonin Vobecky, David Hurych, Oriane Siméoni +4
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, eq…