6 citations · 9 across the 13 of their papers we have counts for
5 papers · 1 filter
CARES: Context-Aware Resolution Selector for VLMs
Moshe Kimhi, Nimrod Shabtay, Raja Giryes +2
Large vision-language models (VLMs) commonly process images at native or high resolution to remain effective across tasks. This inflates visual tokens ofter to 97-99% of total toke…
WAVECLIP: Wavelet Tokenization for Adaptive-Resolution CLIP
Moshe Kimhi, Erez Koifman, Ehud Rivlin +2
We introduce WAVECLIP, a single unified model for adaptive resolution inference in CLIP, enabled by wavelet-based tokenization. WAVECLIP replaces standard patch embeddings with a m…
: Reconstruction, Raw, and Rain: Deraining Directly in the Bayer Domain
Nate Rothschild, Moshe Kimhi, Avi Mendelson +1
Image reconstruction from corrupted images is crucial across many domains. Most reconstruction networks are trained on post-ISP sRGB images, even though the image-signal-processing…
Benchmarking Adversarial Patch Selection and Location
Shai Kimhi, Avi Mendlson, Moshe Kimhi
Adversarial patch attacks threaten the reliability of modern vision models. We present PatchMap, the first spatially exhaustive benchmark of patch placement, built by evaluating ov…
Maximal Brain Damage Without Data or Optimization: Disrupting Neural Networks via Sign-Bit Flips
Ido Galil, Moshe Kimhi, Ran El-Yaniv
Deep Neural Networks (DNNs) can be catastrophically disrupted by flipping only a handful of parameter bits. We introduce Deep Neural Lesion (DNL), a data-free and optimizationfree…