1.5k citations · 1.8k across the 30 of their papers we have counts for
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SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers
Ioannis Kakogeorgiou, Spyros Gidaris, Konstantinos Karantzalos +1
Unsupervised object-centric learning aims to decompose scenes into interpretable object entities, termed slots. Slot-based auto-encoders stand out as a prominent method for this ta…
Three Pillars improving Vision Foundation Model Distillation for Lidar
Gilles Puy, Spyros Gidaris, Alexandre Boulch +5
Self-supervised image backbones can be used to address complex 2D tasks (e.g., semantic segmentation, object discovery) very efficiently and with little or no downstream supervisio…
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…
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…
RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving
Angelika Ando, Spyros Gidaris, Andrei Bursuc +3
Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, e.g., via range projection, is an effective and popular approach. These projection-based methods usuall…