10 papers
CXR-Retrieve: Compositional Text-to-Image Retrieval in Chest Radiography
Tomer Erez, Moshe Kimhi, Chaim Baskin +1
Large chest radiography archives are difficult to search because most studies are paired only with free-text reports rather than structured clinical annotations. Vision-language mo…
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…
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…
Look Where It Matters: High-Resolution Crops Retrieval for Efficient VLMs
Nimrod Shabtay, Moshe Kimhi, Artem Spector +5
Vision-language models (VLMs) typically process images at a native high-resolution, forcing a trade-off between accuracy and computational efficiency: high-resolution inputs captur…
: 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…
Hysteresis Activation Function for Efficient Inference
Moshe Kimhi, Idan Kashani, Avi Mendelson +1
The widely used ReLU is favored for its hardware efficiency, {as the implementation at inference is a one bit sign case,} yet suffers from issues such as the ``dying ReLU'' problem…