8.1k citations · 10.4k across the 29 of their papers we have counts for
6 papers · 2 filters
DyST: Towards Dynamic Neural Scene Representations on Real-World Videos
Maximilian Seitzer, Sjoerd van Steenkiste, Thomas Kipf +2
Visual understanding of the world goes beyond the semantics and flat structure of individual images. In this work, we aim to capture both the 3D structure and dynamics of real-worl…
Video OWL-ViT: Temporally-consistent open-world localization in video
Georg Heigold, Matthias Minderer, Alexey Gritsenko +5
We present an architecture and a training recipe that adapts pre-trained open-world image models to localization in videos. Understanding the open visual world (without being const…
DORSal: Diffusion for Object-centric Representations of Scenes et al
Allan Jabri, Sjoerd van Steenkiste, Emiel Hoogeboom +2
Recent progress in 3D scene understanding enables scalable learning of representations across large datasets of diverse scenes. As a consequence, generalization to unseen scenes an…
Sensitivity of Slot-Based Object-Centric Models to their Number of Slots
Roland S. Zimmermann, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi +2
Self-supervised methods for learning object-centric representations have recently been applied successfully to various datasets. This progress is largely fueled by slot-based metho…
Scaling Vision Transformers to 22 Billion Parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa +39
The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Visio…
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames
Ondrej Biza, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi +3
Automatically discovering composable abstractions from raw perceptual data is a long-standing challenge in machine learning. Recent slot-based neural networks that learn about obje…