  Aims. The treatment of astronomical image time series has won increasing
attention in recent years. Indeed, numerous surveys following up on transient
objects are in progress or under construction, such as the Vera Rubin
Observatory Legacy Survey for Space and Time (LSST), which is poised to produce
huge amounts of these time series. The associated scientific topics are
extensive, ranging from the study of objects in our galaxy to the observation
of the most distant supernovae for measuring the expansion of the universe.
With such a large amount of data available, the need for robust automatic tools
to detect and classify celestial objects is growing steadily. Methods. This
study is based on the assumption that astronomical images contain more
information than light curves. In this paper, we propose a novel approach based
on deep learning for classifying different types of space objects directly
using images. We named our approach ConvEntion, which stands for CONVolutional
attENTION. It is based on convolutions and transformers, which are new
approaches for the treatment of astronomical image time series. Our solution
integrates spatio-temporal features and can be applied to various types of
image datasets with any number of bands. Results. In this work, we solved
various problems the datasets tend to suffer from and we present new results
for classifications using astronomical image time series with an increase in
accuracy of 13%, compared to state-of-the-art approaches that use image time
series, and a 12% increase, compared to approaches that use light curves.
