Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels
- URL: http://arxiv.org/abs/2203.16038v1
- Date: Wed, 30 Mar 2022 03:52:50 GMT
- Title: Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels
- Authors: Jiwon Kim, Kwangrok Ryoo, Junyoung Seo, Gyuseong Lee, Daehwan Kim,
Hansang Cho, Seungryong Kim
- Abstract summary: SemiMatch is a semi-supervised solution for establishing dense correspondences across semantically similar images.
Our framework generates the pseudo-labels using the model's prediction itself between source and weakly-augmented target, and uses pseudo-labels to learn the model again between source and strongly-augmented target.
In experiments, SemiMatch achieves state-of-the-art performance on various benchmarks, especially on PF-Willow by a large margin.
- Score: 26.542718087103665
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Establishing dense correspondences across semantically similar images remains
a challenging task due to the significant intra-class variations and background
clutters. Traditionally, a supervised learning was used for training the
models, which required tremendous manually-labeled data, while some methods
suggested a self-supervised or weakly-supervised learning to mitigate the
reliance on the labeled data, but with limited performance. In this paper, we
present a simple, but effective solution for semantic correspondence that
learns the networks in a semi-supervised manner by supplementing few
ground-truth correspondences via utilization of a large amount of confident
correspondences as pseudo-labels, called SemiMatch. Specifically, our framework
generates the pseudo-labels using the model's prediction itself between source
and weakly-augmented target, and uses pseudo-labels to learn the model again
between source and strongly-augmented target, which improves the robustness of
the model. We also present a novel confidence measure for pseudo-labels and
data augmentation tailored for semantic correspondence. In experiments,
SemiMatch achieves state-of-the-art performance on various benchmarks,
especially on PF-Willow by a large margin.
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