From the 1 of 43 linked papers with an AI index.
2 citations · 2 across the 12 of their papers we have counts for
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When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generators
Krzysztof Adamkiewicz, Brian Bernhard Moser, Stanislav Frolov +3
The paper evaluates modern text-to-image diffusion models as sources of synthetic training data and finds that, despite higher visual quality, newer models produce less diverse ima…
LUMA: Benchmarking Segmentation via a Lightweight Universal Mask Adapter
Tobias Christian Nauen, Anosh Billimoria, Federico Raue +3
Comparing transformer backbones for image segmentation is confounded: each is paired with a different decoder, recipe, and pretraining, so reported differences rarely reflect the b…
ForAug: Mitigating Biases in Image Classification via Controlled Image Compositions
Tobias Christian Nauen, Brian Moser, Federico Raue +2
Large-scale image classification datasets exhibit strong compositional biases: objects tend to be centered, appear at characteristic scales, and co-occur with class-specific contex…
OA-CutMix: Correcting the Label Bias of CutMix
Tobias Christian Nauen, Stanislav Frolov, Federico Raue +2
CutMix has become the de facto standard mixing augmentation, yet its label assignment rests on a flawed assumption: The area of the pasted patch faithfully reflects its semantic co…
SegGuidedNet: Sub-Region-Aware Attention Supervision for Interpretable Brain Tumor Segmentation
Hasaan Maqsood, Saif Ur Rehman Khan, Sebastian Vollmer +2
Accurate segmentation of brain tumour sub-regions from multi-parametric MRI is critical for treatment planning yet remains challenging due to morphological variability, class imbal…
TextTeacher: What Can Language Teach About Images?
Tobias Christian Nauen, Stanislav Frolov, Brian Bernhard Moser +3
The platonic representation hypothesis suggests that sufficiently large models converge to a shared representation geometry, even across modalities. Motivated by this, we ask: Can…