1 citations · 2 across the 4 of their papers we have counts for
5 papers
Moving Forward with Video Saliency: A New Dataset and Benchmark where Motion Matters
Susmit Agrawal, Rebecca Wanner, Juliane Verwiebe +3
Video saliency prediction is inherently harder to model than static image saliency due to the additional temporal dimension. Video saliency benchmarks rest on the premise that pred…
Vision Transformers Learn Gestalt-Like Figure-Ground Cues from Natural Images
Matthias Tangemann, Benjamin Lo, Zygmunt Pizlo +3
Figure-ground organization in the human visual system relies on several shape-based cues, including surroundedness, convexity, and symmetry. While these cues have been extensively…
Only Brains Align with Brains: Cross-Region Alignment Patterns Expose Limits of Normative Models
Larissa Höfling, Matthias Tangemann, Lotta Piefke +3
Neuroscientists and computer vision researchers use model-brain alignment benchmarks to compare artificial and biological vision systems. These benchmarks rank models according to…
Object segmentation from common fate: Motion energy processing enables human-like zero-shot generalization to random dot stimuli
Matthias Tangemann, Matthias Kümmerer, Matthias Bethge
Humans excel at detecting and segmenting moving objects according to the Gestalt principle of "common fate". Remarkably, previous works have shown that human perception generalizes…
Unsupervised Object Learning via Common Fate
Matthias Tangemann, Steffen Schneider, Julius von Kügelgen +6
Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling. We decompose this problem into three easier subtasks, an…