12 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…
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…
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…
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…