7 citations · 7 across the 9 of their papers we have counts for
9 papers
Synthetic Captions for Open-Vocabulary Zero-Shot Segmentation
Tim Lebailly, Vijay Veerabadran, Satwik Kottur +2
Generative vision-language models (VLMs) exhibit strong high-level image understanding but lack spatially dense alignment between vision and language modalities, as our findings in…
Object-Centric Pretraining via Target Encoder Bootstrapping
Nikola Đukić, Tim Lebailly, Tinne Tuytelaars
Object-centric representation learning has recently been successfully applied to real-world datasets. This success can be attributed to pretrained non-object-centric foundation mod…
Collapse-Proof Non-Contrastive Self-Supervised Learning
Emanuele Sansone, Tim Lebailly, Tinne Tuytelaars
We present a principled and simplified design of the projector and loss function for non-contrastive self-supervised learning based on hyperdimensional computing. We theoretically…
A Simple Framework for Open-Vocabulary Zero-Shot Segmentation
Thomas Stegmüller, Tim Lebailly, Nikola Dukic +3
Zero-shot classification capabilities naturally arise in models trained within a vision-language contrastive framework. Despite their classification prowess, these models struggle…
CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping
Tim Lebailly, Thomas Stegmüller, Behzad Bozorgtabar +2
Leveraging nearest neighbor retrieval for self-supervised representation learning has proven beneficial with object-centric images. However, this approach faces limitations when ap…
CrOC: Cross-View Online Clustering for Dense Visual Representation Learning
Thomas Stegmüller, Tim Lebailly, Behzad Bozorgtabar +2
Learning dense visual representations without labels is an arduous task and more so from scene-centric data. We propose to tackle this challenging problem by proposing a Cross-view…
Adaptive Similarity Bootstrapping for Self-Distillation based Representation Learning
Tim Lebailly, Thomas Stegmüller, Behzad Bozorgtabar +2
Most self-supervised methods for representation learning leverage a cross-view consistency objective i.e., they maximize the representation similarity of a given image's augmented…
Global-Local Self-Distillation for Visual Representation Learning
Tim Lebailly, Tinne Tuytelaars
The downstream accuracy of self-supervised methods is tightly linked to the proxy task solved during training and the quality of the gradients extracted from it. Richer and more me…
Motion Prediction Using Temporal Inception Module
Tim Lebailly, Sena Kiciroglu, Mathieu Salzmann +2
Human motion prediction is a necessary component for many applications in robotics and autonomous driving. Recent methods propose using sequence-to-sequence deep learning models to…