Semi-Supervised Object Detection in the Open World
- URL: http://arxiv.org/abs/2307.15710v1
- Date: Fri, 28 Jul 2023 17:59:03 GMT
- Title: Semi-Supervised Object Detection in the Open World
- Authors: Garvita Allabadi, Ana Lucic, Peter Pao-Huang, Yu-Xiong Wang and Vikram
Adve
- Abstract summary: We introduce an ensemble based OOD detector consisting of lightweight auto-encoder networks trained only on ID data.
Our method performs competitively against state-of-the-art OOD detection algorithms and also significantly boosts the semi-supervised learning performance in open-world scenarios.
- Score: 16.274397329511192
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Existing approaches for semi-supervised object detection assume a fixed set
of classes present in training and unlabeled datasets, i.e., in-distribution
(ID) data. The performance of these techniques significantly degrades when
these techniques are deployed in the open-world, due to the fact that the
unlabeled and test data may contain objects that were not seen during training,
i.e., out-of-distribution (OOD) data. The two key questions that we explore in
this paper are: can we detect these OOD samples and if so, can we learn from
them? With these considerations in mind, we propose the Open World
Semi-supervised Detection framework (OWSSD) that effectively detects OOD data
along with a semi-supervised learning pipeline that learns from both ID and OOD
data. We introduce an ensemble based OOD detector consisting of lightweight
auto-encoder networks trained only on ID data. Through extensive evalulation,
we demonstrate that our method performs competitively against state-of-the-art
OOD detection algorithms and also significantly boosts the semi-supervised
learning performance in open-world scenarios.
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