54 citations · 105 across the 14 of their papers we have counts for
7 papers · 1 filter
GG-SSMs: Graph-Generating State Space Models
Nikola Zubić, Davide Scaramuzza
State Space Models (SSMs) are powerful tools for modeling sequential data in computer vision and time series analysis domains. However, traditional SSMs are limited by fixed, one-d…
S7: Selective and Simplified State Space Layers for Sequence Modeling
Taylan Soydan, Nikola Zubić, Nico Messikommer +2
A central challenge in sequence modeling is efficiently handling tasks with extended contexts. While recent state-space models (SSMs) have made significant progress in this area, t…
Reinforcement Learning Meets Visual Odometry
Nico Messikommer, Giovanni Cioffi, Mathias Gehrig +1
Visual Odometry (VO) is essential to downstream mobile robotics and augmented/virtual reality tasks. Despite recent advances, existing VO methods still rely on heuristic design cho…
Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory
Nikola Zubić, Federico Soldá, Aurelio Sulser +1
Despite their successes, deep learning models struggle with tasks requiring complex reasoning and function composition. We present a theoretical and empirical investigation into th…
Event Cameras Meet SPADs for High-Speed, Low-Bandwidth Imaging
Manasi Muglikar, Siddharth Somasundaram, Akshat Dave +3
Traditional cameras face a trade-off between low-light performance and high-speed imaging: longer exposure times to capture sufficient light results in motion blur, whereas shorter…
Hilti SLAM Challenge 2023: Benchmarking Single + Multi-session SLAM across Sensor Constellations in Construction
Ashish Devadas Nair, Julien Kindle, Plamen Levchev +1
Simultaneous Localization and Mapping systems are a key enabler for positioning in both handheld and robotic applications. The Hilti SLAM Challenges organized over the past years h…
State Space Models for Event Cameras
Nikola Zubić, Mathias Gehrig, Davide Scaramuzza
Today, state-of-the-art deep neural networks that process event-camera data first convert a temporal window of events into dense, grid-like input representations. As such, they exh…