5 citations · 9 across the 16 of their papers we have counts for
18 papers
Bar-JEPA: Extracting Values from Bar Chart with Joint-Embedding Predictive Architecture
Poonam Poonam, Alexander Epple, Timo Ropinski
Bar charts are commonly used in data visualization, and while they are easily understood by humans, it is non-trivial to extract the underlying data computationally. For a machine-…
PaCoNet: Deep Data Extraction for Parallel Coordinates
Poonam Poonam, Hannah Kniesel, Pere-Pau Vázquez +1
Extracting data from visualizations has long challenged computer vision, with current research focused on bar, line, and pie charts, among other low-dimensional visualizations. How…
Evaluating Graphical Perception Capabilities of Vision Transformers
Poonam Poonam, Pere-Pau Vázquez, Timo Ropinski
Vision Transformers, ViTs, have emerged as a powerful alternative to convolutional neural networks, CNNs, in a variety of image-based tasks. While CNNs have previously been evaluat…
S2D: Sparse-To-Dense Keymask Distillation for Unsupervised Video Instance Segmentation
Leon Sick, Lukas Hoyer, Dominik Engel +2
In recent years, the state-of-the-art in unsupervised video instance segmentation has heavily relied on synthetic video data, generated from object-centric image datasets such as I…
Unified Semantic Transformer for 3D Scene Understanding
Sebastian Koch, Johanna Wald, Hidenobu Matsuki +3
Holistic 3D scene understanding involves capturing and parsing unstructured 3D environments. Due to the inherent complexity of the real world, existing models have predominantly be…
OpenHype: Hyperbolic Embeddings for Hierarchical Open-Vocabulary Radiance Fields
Lisa Weijler, Sebastian Koch, Fabio Poiesi +2
Modeling the inherent hierarchical structure of 3D objects and 3D scenes is highly desirable, as it enables a more holistic understanding of environments for autonomous agents. Acc…
Weakly Supervised Virus Capsid Detection with Image-Level Annotations in Electron Microscopy Images
Hannah Kniesel, Leon Sick, Tristan Payer +5
Current state-of-the-art methods for object detection rely on annotated bounding boxes of large data sets for training. However, obtaining such annotations is expensive and can req…
Context-Aware Human Behavior Prediction Using Multimodal Large Language Models: Challenges and Insights
Yuchen Liu, Lino Lerch, Luigi Palmieri +4
Predicting human behavior in shared environments is crucial for safe and efficient human-robot interaction. Traditional data-driven methods to that end are pre-trained on domain-sp…
Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets
Hannah Kniesel, Pedro Hermosilla, Timo Ropinski
Recent advances in conditional image generation from diffusion models have shown great potential in achieving impressive image quality while preserving the constraints introduced b…
RelationField: Relate Anything in Radiance Fields
Sebastian Koch, Johanna Wald, Mirco Colosi +4
Neural radiance fields are an emerging 3D scene representation and recently even been extended to learn features for scene understanding by distilling open-vocabulary features from…
CutS3D: Cutting Semantics in 3D for 2D Unsupervised Instance Segmentation
Leon Sick, Dominik Engel, Sebastian Hartwig +2
Traditionally, algorithms that learn to segment object instances in 2D images have heavily relied on large amounts of human-annotated data. Only recently, novel approaches have eme…
Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships
Sebastian Koch, Narunas Vaskevicius, Mirco Colosi +2
Current approaches for 3D scene graph prediction rely on labeled datasets to train models for a fixed set of known object classes and relationship categories. We present Open3DSG,…
A Survey on Quality Metrics for Text-to-Image Generation
Sebastian Hartwig, Dominik Engel, Leon Sick +6
AI-based text-to-image models do not only excel at generating realistic images, they also give designers more and more fine-grained control over the image content. Consequently, th…
Attention-Guided Masked Autoencoders For Learning Image Representations
Leon Sick, Dominik Engel, Pedro Hermosilla +1
Masked autoencoders (MAEs) have established themselves as a powerful method for unsupervised pre-training for computer vision tasks. While vanilla MAEs put equal emphasis on recons…
Lang3DSG: Language-based contrastive pre-training for 3D Scene Graph prediction
Sebastian Koch, Pedro Hermosilla, Narunas Vaskevicius +2
D scene graphs are an emerging 3D scene representation, that models both the objects present in the scene as well as their relationships. However, learning 3D scene graphs is a cha…
SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction
Sebastian Koch, Pedro Hermosilla, Narunas Vaskevicius +2
In the field of 3D scene understanding, 3D scene graphs have emerged as a new scene representation that combines geometric and semantic information about objects and their relation…
Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling
Leon Sick, Dominik Engel, Pedro Hermosilla +1
Traditionally, training neural networks to perform semantic segmentation required expensive human-made annotations. But more recently, advances in the field of unsupervised learnin…
Leveraging Self-Supervised Vision Transformers for Segmentation-based Transfer Function Design
Dominik Engel, Leon Sick, Timo Ropinski
In volume rendering, transfer functions are used to classify structures of interest, and to assign optical properties such as color and opacity. They are commonly defined as 1D or…