Wang, H;
Zhu, C;
Ma, Z;
Oh, C;
(2022)
Improving Generalization of Deep Networks for Estimating Physical Properties of Containers and Fillings.
In:
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.
(pp. pp. 9147-9151).
IEEE: Singapore.
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Abstract
We present methods to estimate the physical properties of household containers and their fillings manipulated by humans. We use a lightweight, pre-trained convolutional neural network with coordinate attention as a backbone model of the pipelines to accurately locate the object of interest and estimate the physical properties in the CORSMAL Containers Manipulation (CCM) dataset. We address the filling type classification with audio data and then combine this information from audio with video modalities to address the filling level classification. For the container capacity, dimension, and mass estimation, we present a data augmentation and consistency measurement to alleviate the over-fitting issue in the CCM dataset caused by the limited number of containers. We augment the training data using an object-of-interest-based re-scaling that increases the variety of physical values of the containers. We then perform the consistency measurement to choose a model with low prediction variance in the same containers under different scenes, which ensures the generalization ability of the model. Our method improves the generalization ability of the models to estimate the property of the containers that were not previously seen in the training.
| Type: | Proceedings paper |
|---|---|
| Title: | Improving Generalization of Deep Networks for Estimating Physical Properties of Containers and Fillings |
| Event: | ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Dates: | 23 May 2022 - 27 May 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICASSP43922.2022.9747349 |
| Publisher version: | https://doi.org/10.1109/ICASSP43922.2022.9747349 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Training , Solid modeling , Three-dimensional displays , Transfer learning , Training data , Containers , Predictive models |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10161226 |
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