  Procedure learning involves identifying the key-steps and determining their
logical order to perform a task. Existing approaches commonly use third-person
videos for learning the procedure, making the manipulated object small in
appearance and often occluded by the actor, leading to significant errors. In
contrast, we observe that videos obtained from first-person (egocentric)
wearable cameras provide an unobstructed and clear view of the action. However,
procedure learning from egocentric videos is challenging because (a) the camera
view undergoes extreme changes due to the wearer's head motion, and (b) the
presence of unrelated frames due to the unconstrained nature of the videos. Due
to this, current state-of-the-art methods' assumptions that the actions occur
at approximately the same time and are of the same duration, do not hold.
Instead, we propose to use the signal provided by the temporal correspondences
between key-steps across videos. To this end, we present a novel
self-supervised Correspond and Cut (CnC) framework for procedure learning. CnC
identifies and utilizes the temporal correspondences between the key-steps
across multiple videos to learn the procedure. Our experiments show that CnC
outperforms the state-of-the-art on the benchmark ProceL and CrossTask datasets
by 5.2% and 6.3%, respectively. Furthermore, for procedure learning using
egocentric videos, we propose the EgoProceL dataset consisting of 62 hours of
videos captured by 130 subjects performing 16 tasks. The source code and the
dataset are available on the project page https://sid2697.github.io/egoprocel/.
