activity
20192022
most citedDeepLab2: A TensorFlow Library for Deep Labeling

34 citations · 42 across the 2 of their papers we have counts for

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6 papers · 1 filter

cs.CV20228 cited

DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation

Aysim Toker, Lukas Kondmann, Mark Weber +10

Earth observation is a fundamental tool for monitoring the evolution of land use in specific areas of interest. Observing and precisely defining change, in this context, requires b…

cs.CV202134 cited

DeepLab2: A TensorFlow Library for Deep Labeling

Mark Weber, Huiyu Wang, Siyuan Qiao +12

DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer…

cs.CV2021

4D Panoptic LiDAR Segmentation

Mehmet Aygün, Aljoša Ošep, Mark Weber +4

Temporal semantic scene understanding is critical for self-driving cars or robots operating in dynamic environments. In this paper, we propose 4D panoptic LiDAR segmentation to ass…

cs.CV2019

Single-Shot Panoptic Segmentation

Mark Weber, Jonathon Luiten, Bastian Leibe

We present a novel end-to-end single-shot method that segments countable object instances (things) as well as background regions (stuff) into a non-overlapping panoptic segmentatio…

cs.CV2019

Visual Person Understanding through Multi-Task and Multi-Dataset Learning

Kilian Pfeiffer, Alexander Hermans, István Sárándi +2

We address the problem of learning a single model for person re-identification, attribute classification, body part segmentation, and pose estimation. With predictions for these ta…

cs.CV2019

4D Generic Video Object Proposals

Aljosa Osep, Paul Voigtlaender, Mark Weber +2

Many high-level video understanding methods require input in the form of object proposals. Currently, such proposals are predominantly generated with the help of networks that were…