![](bga.png)
this time without using manual annotations neither in
the source domain nor in the target one.
6 CONCLUSIONS
In this article, we tackle the problem of determin-
ing the density and the number of objects present in
large sets of images. Building on a CNN-based den-
sity estimator, the proposed methodology can gener-
alize to new data sources for which there are no an-
notations available. We achieve this generalization by
exploiting an Unsupervised Domain Adaptation strat-
egy, whereby a discriminator attached to the output
forces similar density distribution in the target and
source domains. Experiments show a significant im-
provement relative to the performance of the model
without domain adaptation. To the best of our knowl-
edge, we are the first to introduce a UDA scheme for
counting to reduce the gap between the source and the
target domain without using additional labels. Given
the conventional structure of the estimator, the im-
provement obtained by just monitoring the output en-
tails a great capacity to generalize learned knowledge,
thus suggesting the application of similar principles to
the inner layers of the network.
Another contribution is represented by the cre-
ation of two new per-pixel annotated datasets made
available to the scientific community. One of the two
novel datasets is a synthetic dataset created from a
photo-realistic video game. Here the labels are auto-
matically assigned while interacting with the API of
the graphical engine. Using this synthetic dataset, we
demonstrated that it is possible to train a model with a
precisely annotated and automatically generated syn-
thetic dataset and perform UDA toward a real-world
scenario, obtaining very good performance without
using additional manual annotations.
In our view, this work’s outcome opens new per-
spectives to deal with the scalability of learning meth-
ods for large physical systems with scarce supervisory
resources.
ACKNOWLEDGEMENTS
This work was partially supported by H2020 project
AI4EU under GA 825619 and by H2020 project
AI4media under GA 951911.
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