12 citations · 13 across the 10 of their papers we have counts for
10 papers
Structured Diffusion Bridges: Inductive Bias for Denoising Diffusion Bridges
Eitan Kosman, Gabriele Serussi, Chaim Baskin
Modality translation is inherently under-constrained, as multiple cross-modal mappings may yield the same marginals. Recent work has shown that diffusion bridges are effective for…
Towards General Modality Translation with Contrastive and Predictive Latent Diffusion Bridge
Nimrod Berman, Omkar Joglekar, Eitan Kosman +2
Recent advances in generative modeling have positioned diffusion models as state-of-the-art tools for sampling from complex data distributions. While these models have shown remark…
Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction
Aron Distelzweig, Andreas Look, Eitan Kosman +3
In autonomous driving, accurate motion prediction is crucial for safe and efficient motion planning. To ensure safety, planners require reliable uncertainty estimates of the predic…
Motion Forecasting via Model-Based Risk Minimization
Aron Distelzweig, Eitan Kosman, Andreas Look +3
Forecasting the future trajectories of surrounding agents is crucial for autonomous vehicles to ensure safe, efficient, and comfortable route planning. While model ensembling has i…
Bosch Street Dataset: A Multi-Modal Dataset with Imaging Radar for Automated Driving
Karim Armanious, Maurice Quach, Michael Ulrich +25
This paper introduces the Bosch street dataset (BSD), a novel multi-modal large-scale dataset aimed at promoting highly automated driving (HAD) and advanced driver-assistance syste…
Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes
Nimrod Berman, Eitan Kosman, Dotan Di Castro +1
Graph generation is integral to various engineering and scientific disciplines. Nevertheless, existing methodologies tend to overlook the generation of edge attributes. However, we…
Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving
Ali Keysan, Andreas Look, Eitan Kosman +4
In autonomous driving tasks, scene understanding is the first step towards predicting the future behavior of the surrounding traffic participants. Yet, how to represent a given sce…
GraphVid: It Only Takes a Few Nodes to Understand a Video
Eitan Kosman, Dotan Di Castro
We propose a concise representation of videos that encode perceptually meaningful features into graphs. With this representation, we aim to leverage the large amount of redundancie…
LSP : Acceleration and Regularization of Graph Neural Networks via Locality Sensitive Pruning of Graphs
Eitan Kosman, Joel Oren, Dotan Di Castro
Graph Neural Networks (GNNs) have emerged as highly successful tools for graph-related tasks. However, real-world problems involve very large graphs, and the compute resources need…
Vision-Guided Forecasting -- Visual Context for Multi-Horizon Time Series Forecasting
Eitan Kosman, Dotan Di Castro
Autonomous driving gained huge traction in recent years, due to its potential to change the way we commute. Much effort has been put into trying to estimate the state of a vehicle.…