107 citations · 121 across the 11 of their papers we have counts for
12 papers
CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation
Monika Wysoczańska, Oriane Siméoni, Michaël Ramamonjisoa +3
The popular CLIP model displays impressive zero-shot capabilities thanks to its seamless interaction with arbitrary text prompts. However, its lack of spatial awareness makes it un…
Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey
Oriane Siméoni, Éloi Zablocki, Spyros Gidaris +2
The recent enthusiasm for open-world vision systems show the high interest of the community to perform perception tasks outside of the closed-vocabulary benchmark setups which have…
CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Free
Monika Wysoczańska, Michaël Ramamonjisoa, Tomasz Trzciński +1
The emergence of CLIP has opened the way for open-world image perception. The zero-shot classification capabilities of the model are impressive but are harder to use for dense task…
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…
You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentation
Nermin Samet, Oriane Siméoni, Gilles Puy +3
We propose SeedAL, a method to seed active learning for efficient annotation of 3D point clouds for semantic segmentation. Active Learning (AL) iteratively selects relevant data fr…
Unsupervised Object Localization: Observing the Background to Discover Objects
Oriane Siméoni, Chloé Sekkat, Gilles Puy +3
Recent advances in self-supervised visual representation learning have paved the way for unsupervised methods tackling tasks such as object discovery and instance segmentation. How…