Sequential Ensembling for Semantic Segmentation
- URL: http://arxiv.org/abs/2210.05387v1
- Date: Sat, 8 Oct 2022 22:13:59 GMT
- Title: Sequential Ensembling for Semantic Segmentation
- Authors: Rawal Khirodkar, Brandon Smith, Siddhartha Chandra, Amit Agrawal,
Antonio Criminisi
- Abstract summary: We benchmark the popular ensembling approach of combining predictions of multiple, independently-trained, state-of-the-art models.
We propose a novel method inspired by boosting to sequentially ensemble networks that significantly outperforms the naive ensemble baseline.
- Score: 4.030520171276982
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Ensemble approaches for deep-learning-based semantic segmentation remain
insufficiently explored despite the proliferation of competitive benchmarks and
downstream applications. In this work, we explore and benchmark the popular
ensembling approach of combining predictions of multiple,
independently-trained, state-of-the-art models at test time on popular
datasets. Furthermore, we propose a novel method inspired by boosting to
sequentially ensemble networks that significantly outperforms the naive
ensemble baseline. Our approach trains a cascade of models conditioned on class
probabilities predicted by the previous model as an additional input. A key
benefit of this approach is that it allows for dynamic computation offloading,
which helps deploy models on mobile devices. Our proposed novel ADaptive
modulatiON (ADON) block allows spatial feature modulation at various layers
using previous-stage probabilities. Our approach does not require sophisticated
sample selection strategies during training and works with multiple neural
architectures. We significantly improve over the naive ensemble baseline on
challenging datasets such as Cityscapes, ADE-20K, COCO-Stuff, and
PASCAL-Context and set a new state-of-the-art.
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