Efficient Self-Ensemble Framework for Semantic Segmentation
- URL: http://arxiv.org/abs/2111.13280v1
- Date: Fri, 26 Nov 2021 00:35:09 GMT
- Title: Efficient Self-Ensemble Framework for Semantic Segmentation
- Authors: Walid Bousselham, Guillaume Thibault, Lucas Pagano, Archana
Machireddy, Joe Gray, Young Hwan Chang, Xubo Song
- Abstract summary: We propose to leverage the performance boost offered by ensemble methods to enhance semantic segmentation.
Our self-ensemble framework takes advantage of the multi-scale features set produced by feature pyramid network methods.
Our model can be trained end-to-end, alleviating the traditional cumbersome multi-stage training of ensembles.
- Score: 1.0819401241801994
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Ensemble of predictions is known to perform better than individual
predictions taken separately. However, for tasks that require heavy
computational resources, \textit{e.g.} semantic segmentation, creating an
ensemble of learners that needs to be trained separately is hardly tractable.
In this work, we propose to leverage the performance boost offered by ensemble
methods to enhance the semantic segmentation, while avoiding the traditional
heavy training cost of the ensemble. Our self-ensemble framework takes
advantage of the multi-scale features set produced by feature pyramid network
methods to feed independent decoders, thus creating an ensemble within a single
model. Similar to the ensemble, the final prediction is the aggregation of the
prediction made by each learner. In contrast to previous works, our model can
be trained end-to-end, alleviating the traditional cumbersome multi-stage
training of ensembles. Our self-ensemble framework outperforms the current
state-of-the-art on the benchmark datasets ADE20K, Pascal Context and
COCO-Stuff-10K for semantic segmentation and is competitive on Cityscapes. Code
will be available at github.com/WalBouss/SenFormer.
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