6 papers
ReEXplore: Improving MLLMs for Embodied Exploration with Contextualized Retrospective Experience Replay
Gengyuan Zhang, Mingcong Ding, Jingpei Wu +2
Embodied exploration is a target-driven process that requires embodied agents to possess fine-grained perception and knowledge-enhanced decision making. While recent attempts lever…
Improving Perturbation-based Explanations by Understanding the Role of Uncertainty Calibration
Thomas Decker, Volker Tresp, Florian Buettner
Perturbation-based explanations are widely utilized to enhance the transparency of machine-learning models in practice. However, their reliability is often compromised by the unkno…
When and Where do Events Switch in Multi-Event Video Generation?
Ruotong Liao, Guowen Huang, Qing Cheng +3
Text-to-video (T2V) generation has surged in response to challenging questions, especially when a long video must depict multiple sequential events with temporal coherence and cont…
Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations
Thomas Decker, Volker Tresp, Florian Buettner
Perturbation-based explanations are widely utilized to enhance the transparency of modern machine-learning models. However, their reliability is often compromised by the unknown mo…
AViLA: Asynchronous Vision-Language Agent for Streaming Multimodal Data Interaction
Gengyuan Zhang, Tanveer Hannan, Hermine Kleiner +6
An ideal vision-language agent serves as a bridge between the human users and their surrounding physical world in real-world applications like autonomous driving and embodied agent…
Incremental Uncertainty-aware Performance Monitoring with Active Labeling Intervention
Alexander Koebler, Thomas Decker, Ingo Thon +2
We study the problem of monitoring machine learning models under gradual distribution shifts, where circumstances change slowly over time, often leading to unnoticed yet significan…