9 citations · 10 across the 7 of their papers we have counts for
8 papers
Semantic Allocation in Ordered Bottlenecks: Predictive Residual Inference for Visual Representation Learning
Erik Ayari, Manuel Traub, Martin V. Butz
Ordered bottlenecks aim to provide utility at flexible budgets by assigning coarse information to early tokens and task-relevant detail to later ones. Prior work, including tail dr…
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…
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…
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.…
Learning What and Where: Disentangling Location and Identity Tracking Without Supervision
Manuel Traub, Sebastian Otte, Tobias Menge +3
Our brain can almost effortlessly decompose visual data streams into background and salient objects. Moreover, it can anticipate object motion and interactions, which are crucial a…
Many-Joint Robot Arm Control with Recurrent Spiking Neural Networks
Manuel Traub, Robert Legenstein, Sebastian Otte
In the paper, we show how scalable, low-cost trunk-like robotic arms can be constructed using only basic 3D-printing equipment and simple electronics. The design is based on unifor…
Learning Precise Spike Timings with Eligibility Traces
Manuel Traub, Martin V. Butz, R. Harald Baayen +1
Recent research in the field of spiking neural networks (SNNs) has shown that recurrent variants of SNNs, namely long short-term SNNs (LSNNs), can be trained via error gradients ju…
Towards Automatic Embryo Staging in 3D+T Microscopy Images using Convolutional Neural Networks and PointNets
Manuel Traub, Johannes Stegmaier
Automatic analyses and comparisons of different stages of embryonic development largely depend on a highly accurate spatiotemporal alignment of the investigated data sets. In this…