32 citations · 100 across the 47 of their papers we have counts for
9 papers · 2 filters
SoftCache: Adaptive Step Size Caching for Diffusion Transformers
Shimon Malnick, Nadav Orenstein, Ohad Fried +1
Diffusion and flow models generate a sample over iterative steps, each a full pass through a large network, and that pass dominates generation cost. Training-free caching accelerat…
Texture Interpolation as Texton Matching
Ido Madar, Gal Oren, Shai Avidan
Texture interpolation asks what should lie between two textures. Standard methods typically blend pixels or features at corresponding spatial locations, implicitly assuming that co…
Web Scale on a Budget: Tail-Aware Retrieval Evaluation
Boaz Meivar, Shai Avidan
A common practice in instance retrieval is to grow the gallery size to improve a benchmark's discrimination capability. One prominent example is ILIAS, which adds up to 100 million…
Scalable Black-Box Model Attribution for Images
Asaf Livne, Amir Jevnisek, Shai Avidan
The rapid proliferation of generative models raises the model attribution problem: given only an image, can we determine which model produced it? We propose a lightweight CNN to so…
Ill-Posed by Design: Probing Evidence Use in VLMs
Boaz Meivar, Shaked Perek, Shani Shvartzman +2
Counterfactual analysis is widely used to study evidence use in vision-language models, but its diagnostic value is limited on well-posed tasks: when several cues independently sup…
Where Does Texture Evidence Live in SAM? Features, Proposal Masks, and Texture Segmentation
Nadav Orenstein, Aviad Cohen Zada, Shai Avidan +1
Texture segmentation stresses foundation segmentation because meaningful regions are defined by material or repeated appearance rather than object identity. Segment Anything Models…
Sub-Semantic Image Segmentation
Aviad Cohen Zada, Nadav Orenstein, Shai Avidan +1
Images can be segmented based on visual cues (i.e., texture segmentation) or into objects (i.e., semantic segmentation). We propose a new category of sub-semantic image segmentatio…
Optimal Transport Flow Matching by Design
Shimon Malnick, Matan Rusanovsky, Ohad Fried +1
Flow matching models learn to transport samples from a simple prior distribution to a complex data distribution. When prior-data pairs are coupled via optimal transport (OT), the l…
Scene Grounding In the Wild
Tamir Cohen, Leo Segre, Shay Shomer-Chai +2
Reconstructing accurate 3D models of large-scale real-world scenes from unstructured, in-the-wild imagery remains a core challenge in computer vision, especially when the input vie…