6 citations · 11 across the 6 of their papers we have counts for
4 papers · 1 filter
Mind the Detail: Uncovering Clinically Relevant Image Details in Accelerated MRI with Semantically Diverse Reconstructions
Jan Nikolas Morshuis, Christian Schlarmann, Thomas Küstner +2
In recent years, accelerated MRI reconstruction based on deep learning has led to significant improvements in image quality with impressive results for high acceleration factors. H…
Robustness in Both Domains: CLIP Needs a Robust Text Encoder
Elias Abad Rocamora, Christian Schlarmann, Naman Deep Singh +3
Adversarial input attacks can cause a significant shift of CLIP embeddings. This can affect the downstream robustness of models incorporating CLIP in the pipeline, such as text-to-…
FuseLIP: Multimodal Embeddings via Early Fusion of Discrete Tokens
Christian Schlarmann, Francesco Croce, Nicolas Flammarion +1
Contrastive language-image pre-training aligns features of text-image pairs in a common latent space via distinct encoders for each modality. While this approach achieves impressiv…
Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual Metrics
Francesco Croce, Christian Schlarmann, Naman Deep Singh +1
Measuring perceptual similarity is a key tool in computer vision. In recent years perceptual metrics based on features extracted from neural networks with large and diverse trainin…