10 citations · 10 across the 19 of their papers we have counts for
20 papers
OvDSGG: End-to-End Open-Vocabulary Dynamic Scene Graph Generation
John Helsby, Yi Yang, Bodo Rosenhahn +1
Dynamic scene graphs (DSGs) capture spatio-temporal interactions across videos as subject, predicate, object triplets, and underpin downstream tasks such as video…
Seeing Before Answering: Training-Free Visual Layer Profiling for Vision-Language Models
Ruchen Liu, Yi Yang, Yiming Xu +3
LLaVA-style Vision-Language Models (VLMs) pass visual tokens from a fixed late layer of the vision backbone, typically the penultimate one, to the language model. We first show tha…
Compiling VGDL into Causal Models
Mohit Jiwatode, Bodo Rosenhahn, Alexander Dockhorn
Reinforcement learning and large language models often struggle to accurately capture the causal mechanics of game environments. Standard reinforcement learning agents tend to rely…
Spatial Reasoning in LLM Game Agents: Impact of Causal Context and Multi-Step Planning
Mohit Jiwatode, Ronja Fuchs, Robin Schmöcker +2
LLM-based game agents often perform poorly on more complex tasks. This work examines whether these failures are linked to limited spatial reasoning and evaluates whether causal pro…
PUF: Plug-and-Play Uncertainty-Aware Fusion for Online 3D Scene Graph Generation
Yi Yang, Myrna Castillo, Bodo Rosenhahn +1
Online 3D scene graph generation builds a persistent, structured representation of a scene by incrementally fusing 2D observations into a global 3D graph. Existing online methods t…
MATCH: Flow Matching for Multi-View Anomaly Detection
Mathis Kruse, Melissa Schween, Bodo Rosenhahn
Detecting anomalies in industrial objects is an important topic for increasing production efficiency. More complex objects often require the analysis of several view points, which…
DINO-QPM: Adapting Visual Foundation Models for Globally Interpretable Image Classification
Robert Zimmermann, Thomas Norrenbrock, Bodo Rosenhahn
Although visual foundation models like DINOv2 provide state-of-the-art performance as feature extractors, their complex, high-dimensional representations create substantial hurdles…
Video Patch Pruning: Efficient Video Instance Segmentation via Early Token Reduction
Patrick Glandorf, Thomas Norrenbrock, Bodo Rosenhahn
Vision Transformers (ViTs) have demonstrated state-ofthe-art performance in several benchmarks, yet their high computational costs hinders their practical deployment. Patch Pruning…
BUSSARD: Normalizing Flows for Bijective Universal Scene-Specific Anomalous Relationship Detection
Melissa Schween, Mathis Kruse, Bodo Rosenhahn
We propose Bijective Universal Scene-Specific Anomalous Relationship Detection (BUSSARD), a normalizing flow-based model for detecting anomalous relations in scene graphs, generate…
From Gameplay Traces to Game Mechanics: Causal Induction with Large Language Models
Mohit Jiwatode, Alexander Dockhorn, Bodo Rosenhahn
Deep learning agents can achieve high performance in complex game domains without often understanding the underlying causal game mechanics. To address this, we investigate Causal I…
Multi-Object Tracking Retrieval with LLaVA-Video: A Training-Free Solution to MOT25-StAG Challenge
Yi Yang, Yiming Xu, Timo Kaiser +3
In this report, we present our solution to the MOT25-Spatiotemporal Action Grounding (MOT25-StAG) Challenge. The aim of this challenge is to accurately localize and track multiple…
Discovering State Equivalences in UCT Search Trees By Action Pruning
Robin Schmöcker, Alexander Dockhorn, Bodo Rosenhahn
One approach to enhance Monte Carlo Tree Search (MCTS) is to improve its sample efficiency by grouping/abstracting states or state-action pairs and sharing statistics within a grou…
Grouping Nodes With Known Value Differences: A Lossless UCT-based Abstraction Algorithm
Robin Schmöcker, Alexander Dockhorn, Bodo Rosenhahn
A core challenge of Monte Carlo Tree Search (MCTS) is its sample efficiency, which can be improved by grouping state-action pairs and using their aggregate statistics instead of si…
Investigating Intra-Abstraction Policies For Non-exact Abstraction Algorithms
Robin Schmöcker, Alexander Dockhorn, Bodo Rosenhahn
One weakness of Monte Carlo Tree Search (MCTS) is its sample efficiency which can be addressed by building and using state and/or action abstractions in parallel to the tree search…
AUPO -- Abstracted Until Proven Otherwise: A Reward Distribution Based Abstraction Algorithm
Robin Schmöcker, Alexander Dockhorn, Bodo Rosenhahn
We introduce a novel, drop-in modification to Monte Carlo Tree Search's (MCTS) decision policy that we call AUPO. Comparisons based on a range of IPPC benchmark problems show that…
FDSG: Forecasting Dynamic Scene Graphs
Yi Yang, Yuren Cong, Hao Cheng +2
Dynamic scene graph generation extends scene graph generation from images to videos by modeling entity relationships and their temporal evolution. However, existing methods either…
Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentation
Mariia Khan, Yue Qiu, Yuren Cong +3
Multi-class multi-instance segmentation is the task of identifying masks for multiple object classes and multiple instances of the same class within an image. The foundational Segm…
Robust Shape Fitting for 3D Scene Abstraction
Florian Kluger, Eric Brachmann, Michael Ying Yang +1
Humans perceive and construct the world as an arrangement of simple parametric models. In particular, we can often describe man-made environments using volumetric primitives such a…
PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus
Florian Kluger, Bodo Rosenhahn
We present a real-time method for robust estimation of multiple instances of geometric models from noisy data. Geometric models such as vanishing points, planar homographies or fun…
FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing
Yuren Cong, Mengmeng Xu, Christian Simon +7
Text-to-video editing aims to edit the visual appearance of a source video conditional on textual prompts. A major challenge in this task is to ensure that all frames in the edited…