1 citations · 1 across the 2 of their papers we have counts for
5 papers
Reconstructing Training Data From Real World Models Trained with Transfer Learning
Yakir Oz, Gilad Yehudai, Gal Vardi +3
Current methods for reconstructing training data from trained classifiers are restricted to very small models, limited training set sizes, and low-resolution images. Such restricti…
Reconstructing Training Data from Multiclass Neural Networks
Gon Buzaglo, Niv Haim, Gilad Yehudai +2
Reconstructing samples from the training set of trained neural networks is a major privacy concern. Haim et al. (2022) recently showed that it is possible to reconstruct training s…
Teaching CLIP to Count to Ten
Roni Paiss, Ariel Ephrat, Omer Tov +4
Large vision-language models (VLMs), such as CLIP, learn rich joint image-text representations, facilitating advances in numerous downstream tasks, including zero-shot classificati…
Combining Internal and External Constraints for Unrolling Shutter in Videos
Eyal Naor, Itai Antebi, Shai Bagon +1
Videos obtained by rolling-shutter (RS) cameras result in spatially-distorted frames. These distortions become significant under fast camera/scene motions. Undoing effects of RS is…
Temporal-Needle: A view and appearance invariant video descriptor
Michal Yarom, Michal Irani
The ability to detect similar actions across videos can be very useful for real-world applications in many fields. However, this task is still challenging for existing systems, sin…