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cs.CV2025
Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation
Manuel Traub, Martin V. Butz
Current state-of-the-art segmentation models encode entire images before focusing on specific objects. This wastes computational resources. We introduce FLIP (Fovea-Like Input Patc…
cs.CV2023
Loci-Segmented: Improving Scene Segmentation Learning
Manuel Traub, Frederic Becker, Adrian Sauter +2
Current slot-oriented approaches for compositional scene segmentation from images and videos rely on provided background information or slot assignments. We present a segmented loc…
cs.CV2023
Learning Object Permanence from Videos via Latent Imaginations
Manuel Traub, Frederic Becker, Sebastian Otte +1
While human infants exhibit knowledge about object permanence from two months of age onwards, deep-learning approaches still largely fail to recognize objects' continued existence.…