1 research outputs found
Continual egocentric object recognition
We present a framework capable of tackilng the problem of continual object
recognition in a setting which resembles that under whichhumans see and learn.
This setting has a set of unique characteristics:it assumes an egocentric
point-of-view bound to the needs of a singleperson, which implies a relatively
low diversity of data and a coldstart with no data; it requires to operate in
an open world, where newobjects can be encounteredat any time; supervision is
scarce and hasto be solicited to the user, and completelyunsupervised
recognitionof new objects should be possible. Note that this setting differs
fromthe one addressed in the open world recognition literature, where
supervised feedback is always requested to be able to incorporate newobjects.
We propose a first solution to this problem in the form ofa memory-based
incremental framework that is capable of storinginformation of each and any
object it encounters, while using the supervision of the user to learn to
discriminate between known and unknown objects. Our approach is based on four
main features: the useof time and space persistence (i.e., the appearance of
objects changesrelatively slowly), the use of similarity as the main driving
principlefor object recognition and novelty detection, the progressive
introduction of new objects in a developmental fashion and the
selectiveelicitation of user feedback in an online active learning fashion.
Experimental results show the feasibility of open world, generic
objectrecognition, the ability to recognize, memorize and re-identify
newobjects even in complete absence of user supervision, and the utilityof
persistence and incrementality in boosting performance