activity
20142025
most citedPedestrian Motion Direction Estimation Using Simulated Automotive MIMO Radar

39 citations · 49 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025

A Comparative Study of Vision Transformers and CNNs for Few-Shot Rigid Transformation and Fundamental Matrix Estimation

Alon Kaya, Igal Bilik, Inna Stainvas

Vision-transformers (ViTs) and large-scale convolution-neural-networks (CNNs) have reshaped computer vision through pretrained feature representations that enable strong transfer l…

cs.CV2025★ 8 cited

RGBX-DiffusionDet: A Framework for Multi-Modal RGB-X Object Detection Using DiffusionDet

Eliraz Orfaig, Inna Stainvas, Igal Bilik

This work introduces RGBX-DiffusionDet, an object detection framework extending the DiffusionDet model to fuse the heterogeneous 2D data (X) with RGB imagery via an adaptive multim…

eess.SP2024

SuperResolution Radar Gesture Recognitio

Netanel Blumenfeld, Inna Stainvas, Igal Bilik

"This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible." Driver's int…

cs.CV2024★ 1 cited

Enhanced Automotive Object Detection via RGB-D Fusion in a DiffusionDet Framework

Eliraz Orfaig, Inna Stainvas, Igal Bilik

Vision-based autonomous driving requires reliable and efficient object detection. This work proposes a DiffusionDet-based framework that exploits data fusion from the monocular cam…

eess.SP2018★ 39 cited

Pedestrian Motion Direction Estimation Using Simulated Automotive MIMO Radar

Petro Khomchuk, Inna Stainvas, Igal Bilik

Micro-Doppler-based target classification capabilities of the automotive radars can provide high reliability and short latency to the future active safety automotive features. A la…

cs.CV2014★ 1 cited

Cancer Detection with Multiple Radiologists via Soft Multiple Instance Logistic Regression and Regularization

Inna Stainvas, Alexandra Manevitch, Isaac Leichter

This paper deals with the multiple annotation problem in medical application of cancer detection in digital images. The main assumption is that though images are labeled by many ex…