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cs.CV2025

SIS-Challenge: Event-based Spatio-temporal Instance Segmentation Challenge at the CVPR 2025 Event-based Vision Workshop

Friedhelm Hamann, Emil Mededovic, Fabian Gülhan +21

We present an overview of the Spatio-temporal Instance Segmentation (SIS) challenge held in conjunction with the CVPR 2025 Event-based Vision Workshop. The task is to predict accur…

cs.CV2025

Unsupervised Joint Learning of Optical Flow and Intensity with Event Cameras

Shuang Guo, Friedhelm Hamann, Guillermo Gallego

Event cameras rely on motion to obtain information about scene appearance. This means that appearance and motion are inherently linked: either both are present and recorded in the…

cs.CV2025

Event-based Continuous Color Video Decompression from Single Frames

Ziyun Wang, Friedhelm Hamann, Kenneth Chaney +3

We present ContinuityCam, a novel approach to generate a continuous video from a single static RGB image and an event camera stream. Conventional cameras struggle with high-speed m…

cs.CV2025

ETAP: Event-based Tracking of Any Point

Friedhelm Hamann, Daniel Gehrig, Filbert Febryanto +2

Tracking any point (TAP) recently shifted the motion estimation paradigm from focusing on individual salient points with local templates to tracking arbitrary points with global im…

cs.CV2024

Fourier-based Action Recognition for Wildlife Behavior Quantification with Event Cameras

Friedhelm Hamann, Suman Ghosh, Ignacio Juarez Martinez +3

Event cameras are novel bio-inspired vision sensors that measure pixel-wise brightness changes asynchronously instead of images at a given frame rate. They offer promising advantag…

cs.CV2024

MouseSIS: A Frames-and-Events Dataset for Space-Time Instance Segmentation of Mice

Friedhelm Hamann, Hanxiong Li, Paul Mieske +2

Enabled by large annotated datasets, tracking and segmentation of objects in videos has made remarkable progress in recent years. Despite these advancements, algorithms still strug…