Online Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions

Panagiotakopoulos, Theodoros; Dovesi, Pier Luigi; Härenstam-Nielsen, Linus; Poggi, Matteo

doi:10.1007/978-3-031-19830-4_8

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Cited by 10 publications

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“…Future Work. We foresee federated adaptation will be applied to other visual tasks for which online adaptation is a reality, such as single image depth estimation [73], optical flow [39] or semantic segmentation [4,40].…”

Section: Discussionmentioning

confidence: 99%

Self-adapting Confidence Estimation for Stereo

Poggi

Aleotti

Tosi

et al. 2020

Lecture Notes in Computer Science

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Project page: https://fedstereo.github.io/ ~80 FPS 13.86% D1-all 9.16% D1-all 8.36% D1-all ~10 FPS ~80 FPS (a) (b) (c) (d) Figure 1. Federated adaptation in challenging environments. When facing a domain very different from those observed during training -e.g., nighttime images (a) -stereo models [55] suffer drops in accuracy (b). By enabling online adaptation [41] (c) the network can improve its predictions, at the expense of decimating the framerate. In our federated framework, the model can demand the adaptation process to the cloud, to enjoy its benefits while maintaining the original processing speed (d).

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Section: Discussionmentioning

confidence: 99%