  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.
