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cs.CV2026
MotionJEPA: Preventing Temporal Feature Collapse by Capturing Visual Changes in Latent Space
Markus Karmann, Shile Li, Christian Internò +10
Joint Embedding Predictive Architectures (JEPAs) are a promising paradigm for learning task-agnostic latent world models without visual reconstruction. However, standard JEPA train…
cs.CV2026
Joint Post-Training Quantization of Vision Transformers with Learned Prompt-Guided Data Generation
Shile Li, Markus Karmann, Onay Urfalioglu
We present a framework for end-to-end joint quantization of Vision Transformers trained on ImageNet for the purpose of image classification. Unlike prior post-training or block-wis…