MFASSL adds mirror-paired views, a lightweight Mirror-Fusion Attention module, and reflection-consistency losses to improve SSL on bilateral data with ~2.7% extra parameters.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.CV 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
An asymmetric multi-level distillation framework lets a student ViT approximate clean-image representations from distorted inputs alone, outperforming prior methods on classification under distortions.
A sequential-to-global SSL method based on DINO pretrains iterative foveal-inspired vision transformers to achieve competitive ImageNet-1K performance with constant compute regardless of input resolution.
Self-supervised contrastive learning adapts ViT for cardiac MR classification, outperforming supervised training with AUC >0.75 on four common sequences and generalization to BraTS and ADNI.
citing papers explorer
-
Mirror-Fusion Attention for Reflection-Aware Self-Supervised Representation Learning
MFASSL adds mirror-paired views, a lightweight Mirror-Fusion Attention module, and reflection-consistency losses to improve SSL on bilateral data with ~2.7% extra parameters.
-
Distilling Vision Transformers for Distortion-Robust Representation Learning
An asymmetric multi-level distillation framework lets a student ViT approximate clean-image representations from distorted inputs alone, outperforming prior methods on classification under distortions.
-
Self-supervised pretraining for an iterative image size agnostic vision transformer
A sequential-to-global SSL method based on DINO pretrains iterative foveal-inspired vision transformers to achieve competitive ImageNet-1K performance with constant compute regardless of input resolution.
-
Self-Supervised Contrastive Learning for Cardiac MR Sequence Classification
Self-supervised contrastive learning adapts ViT for cardiac MR classification, outperforming supervised training with AUC >0.75 on four common sequences and generalization to BraTS and ADNI.