29 citations · 37 across the 8 of their papers we have counts for
10 papers
From Sparse X-rays to 3D CT: Training-Free Reconstruction with Diffusion Priors
Zhenkai Zhang, Markus Hiller, Krista A. Ehinger +1
Solving 3D medical inverse problems typically requires training dedicated supervised models for each specific task and measurement setting. To break this dependency, we present TF-…
Pixel-Level Residual Diffusion Transformer: Scalable 3D CT Volume Generation
Zhenkai Zhang, Markus Hiller, Krista A. Ehinger +1
Generating high-resolution 3D CT volumes with fine details remains challenging due to substantial computational demands and optimization difficulties inherent to existing generativ…
SVoT: State-aware Visualization-of-Thought for Spatial Reasoning via Reinforcement Learning
Chao Lei, Yanbei Jiang, Markus Hiller +4
Spatial reasoning remains a challenge for Multimodal Large Language Models (MLLMs), as it requires reliable multi-hop inference over both intermediate states and state transitions.…
Active Budget Allocation for Efficient Scaling Law Estimation via Surrogate-Guided Pruning
Viktoria Schram, Markus Hiller, Daniel Beck +1
Predicting model performance at larger scales enables the design of training strategies and architectures tailored to specific performance targets. Empirical scaling law research i…
Zero-Shot Performance Prediction for Probabilistic Scaling Laws
Viktoria Schram, Markus Hiller, Daniel Beck +1
The prediction of learning curves for Natural Language Processing (NLP) models enables informed decision-making to meet specific performance objectives, while reducing computationa…
Perceiving Longer Sequences With Bi-Directional Cross-Attention Transformers
Markus Hiller, Krista A. Ehinger, Tom Drummond
We present a novel bi-directional Transformer architecture (BiXT) which scales linearly with input size in terms of computational cost and memory consumption, but does not suffer t…
Rethinking Generalization in Few-Shot Classification
Markus Hiller, Rongkai Ma, Mehrtash Harandi +1
Single image-level annotations only correctly describe an often small subset of an image's content, particularly when complex real-world scenes are depicted. While this might be ac…
On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation
Markus Hiller, Mehrtash Harandi, Tom Drummond
Inspired by the concept of preconditioning, we propose a novel method to increase adaptation speed for gradient-based meta-learning methods without incurring extra parameters. We d…
Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers
Tianyu Zhu, Markus Hiller, Mahsa Ehsanpour +4
Tracking a time-varying indefinite number of objects in a video sequence over time remains a challenge despite recent advances in the field. Most existing approaches are not able t…
Learning Topometric Semantic Maps from Occupancy Grids
Markus Hiller, Chen Qiu, Florian Particke +2
Today's mobile robots are expected to operate in complex environments they share with humans. To allow intuitive human-robot collaboration, robots require a human-like understandin…