Please see the repositories of our group Vision-and-Sensing Lab at Github



DXAI: Explaining Classification by Image Decomposition

Elnatan Kadar, Guy Gilboa arxiv preprint We propose a new way to explain and to visualize neural network classification through a decomposition-based explainable AI (DXAI). Instead of providing an explanation heatmap, our method yields a decomposition of the image into class-agnostic and class-distinct parts, with respect to the data and chosen classifier. Following a fundamental […]

Critical Points ++: An Agile Point Cloud Importance Measure for Robust Classification, Adversarial Defense and Explainable AI

Yossef Meir Levi, Guy Gilboa The ability to cope accurately and fast with Out-Of-Distribution (OOD) samples is crucial in real-world safety demanding applications. In this work we first study the interplay between critical points of 3D point clouds and OOD samples. Our findings are that common corruptions and outliers are often interpreted as critical points. […]

EPiC: Ensemble of Partial Point Clouds for Robust Classification, ICCV 2023

Meir Yossef Levi, Guy Gilboa Accepted to ICCV 2023. Robust point cloud classification is crucial for real-world applications, as consumer-type 3D sensors often yield partial and noisy data, degraded by various artifacts. In this work we propose a general ensemble framework, based on partial point cloud sampling. Each ensemble member is exposed to only partial […]

BASiS: Batch Aligned Spectral Embedding Space, CVPR 2023

Or Streicher, Ido Cohen, Guy Gilboa; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 10396-10405 CVPR Repository Arxiv preprint  Graph is a highly generic and diverse representation, suitable for almost any data processing problem. Spectral graph theory has been shown to provide powerful algorithms, backed by solid linear algebra […]

Graph Laplacian for Semi-Supervised Learning, accepted to SSVM-2023

Or Streicher, Guy Gilboa, accepted to SSVM 2023 (oral) 9th International Conference, SSVM 2023, Santa Margherita di Pula, Italy, May 21–25, 2023, Proceedings, Springer LNCS 14009, pp. 250-262, 2023. Springer conference proceedings Arxiv preprint Abatract Semi-supervised learning is highly useful in common scenarios where labeled data is scarce but unlabeled data is abundant. The graph […]


Spectral Total Variation

Matlab code for spectral total variation filtering for grayscale and color images. Here a band-stop example is shown (removing selected bands of textures). Code for grayscale Spectral TV Code for color Spectral TV Run: demo_ss_freq_tv_texture.m or demo_specTV_color_orange.m Ref: G. Gilboa, “A total variation spectral framework for scale and texture analysis.” SIAM Journal on Imaging Sciences 7.4 […]


HoRA 3D Robust Features

See full details here. Based on MSc thesis of Guy Berdugo G. Berdugo,  ”3D Correspondences By Local Feature Matching”, M.Sc. Thesis, Technion, 2017.


DROT 3D Multiple Camera Still and Motion Dataset

See full details here. DROT is a depth dataset created to test depth restoration, rectification and upsampling methods. D. Rotman and G. Gilboa, “A depth restoration occlusionless temporal dataset,” in International Conference on 3D Vision (3DV). IEEE, 2016.


Flows Generating Nonlinear Eigenfunctions

See full details here


Blind Facial Image Quality Enhancement using Non-Rigid Semantic Patches

See full details here.  


Non-local Diffusion

Matlab code for non-local diffusion for image denoising. Run: demo_nl_diff.m Refs: [1] Gilboa, Guy, and Stanley Osher. “Nonlocal linear image regularization and supervised segmentation.” Multiscale Modeling & Simulation 6.2 (2007): 595-630. [2] Gilboa, Guy, and Stanley Osher. “Nonlocal operators with applications to image processing.” Multiscale Modeling & Simulation 7.3 (2008): 1005-1028.