23 citations · 47 across the 27 of their papers we have counts for
11 papers · 1 filter
Incremental Learning of Affordances using Markov Logic Networks
George Potter, Gertjan Burghouts, Joris Sijs
Affordances enable robots to have a semantic understanding of their surroundings. This allows them to have more acting flexibility when completing a given task. Capturing object af…
Better Language Models Exhibit Higher Visual Alignment
Jona Ruthardt, Gertjan J. Burghouts, Serge Belongie +1
How well do text-only large language models (LLMs) align with the visual world? We present a systematic evaluation of this question by incorporating frozen representations of vario…
Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation
Fieke Hillerstrom, Gertjan Burghouts
Many inductive logic programming (ILP) methods are incapable of learning programs from probabilistic background knowledge, e.g. coming from sensory data or neural networks with pro…
Anticipating Future Object Compositions without Forgetting
Youssef Zahran, Gertjan Burghouts, Yke Bauke Eisma
Despite the significant advancements in computer vision models, their ability to generalize to novel object-attribute compositions remains limited. Existing methods for Composition…
Near, far: Patch-ordering enhances vision foundation models' scene understanding
Valentinos Pariza, Mohammadreza Salehi, Gertjan Burghouts +2
We introduce NeCo: Patch Neighbor Consistency, a novel self-supervised training loss that enforces patch-level nearest neighbor consistency across a student and teacher model. Comp…
Which objects help me to act effectively? Reasoning about physically-grounded affordances
Anne Kemmeren, Gertjan Burghouts, Michael van Bekkum +2
For effective interactions with the open world, robots should understand how interactions with known and novel objects help them towards their goal. A key aspect of this understand…
Lightweight Uncertainty Quantification with Simplex Semantic Segmentation for Terrain Traversability
Judith Dijk, Gertjan Burghouts, Kapil D. Katyal +5
For navigation of robots, image segmentation is an important component to determining a terrain's traversability. For safe and efficient navigation, it is key to assess the uncerta…
Open-World Visual Reasoning by a Neuro-Symbolic Program of Zero-Shot Symbols
Gertjan Burghouts, Fieke Hillerström, Erwin Walraven +5
We consider the problem of finding spatial configurations of multiple objects in images, e.g., a mobile inspection robot is tasked to localize abandoned tools on the floor. We defi…
Affordance Perception by a Knowledge-Guided Vision-Language Model with Efficient Error Correction
Gertjan Burghouts, Marianne Schaaphok, Michael van Bekkum +3
Mobile robot platforms will increasingly be tasked with activities that involve grasping and manipulating objects in open world environments. Affordance understanding provides a ro…
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
Miltiadis Kofinas, Boris Knyazev, Yan Zhang +5
Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural netwo…
Language-Based Augmentation to Address Shortcut Learning in Object Goal Navigation
Dennis Hoftijzer, Gertjan Burghouts, Luuk Spreeuwers
Deep Reinforcement Learning (DRL) has shown great potential in enabling robots to find certain objects (e.g., `find a fridge') in environments like homes or schools. This task is k…