  In the last few years we have seen a growing interest in machine learning
approaches to computer vision and, especially, to semantic labeling. Nowadays
state of the art systems use deep learning on millions of labeled images with
very successful results on benchmarks, though it is unlikely to expect similar
results in unrestricted visual environments. Most learning schemes essentially
ignore the inherent sequential structure of videos: this might be a critical
issue, since any visual recognition process is remarkably more complex when
shuffling video frames. Based on this remark, we propose a re-foundation of the
communication protocol between visual agents and the environment, which is
referred to as learning to see like children. Like for human interaction,
visual concepts are acquired by the agents solely by processing their own
visual stream along with human supervisions on selected pixels. We give a proof
of concept that remarkable semantic labeling can emerge within this protocol by
using only a few supervised examples. This is made possible by exploiting a
constraint of motion coherent labeling that virtually offers tons of
supervisions. Additional visual constraints, including those associated with
object supervisions, are used within the context of learning from constraints.
The framework is extended in the direction of lifelong learning, so as our
visual agents live in their own visual environment without distinguishing
learning and test set. Learning takes place in deep architectures under a
progressive developmental scheme. In order to evaluate our Developmental Visual
Agents (DVAs), in addition to classic benchmarks, we open the doors of our lab,
allowing people to evaluate DVAs by crowd-sourcing. Such assessment mechanism
might result in a paradigm shift in methodologies and algorithms for computer
vision, encouraging truly novel solutions within the proposed framework.
