  Advances in computing have enabled widespread access to pose estimation,
creating new sources of data streams. Unlike mock set-ups for data collection,
tapping into these data streams through on-device active learning allows us to
directly sample from the real world to improve the spread of the training
distribution. However, on-device computing power is limited, implying that any
candidate active learning algorithm should have a low compute footprint while
also being reliable. Although multiple algorithms cater to pose estimation,
they either use extensive compute to power state-of-the-art results or are not
competitive in low-resource settings. We address this limitation with VL4Pose
(Visual Likelihood For Pose Estimation), a first principles approach for active
learning through out-of-distribution detection. We begin with a simple premise:
pose estimators often predict incoherent poses for out-of-distribution samples.
Hence, can we identify a distribution of poses the model has been trained on,
to identify incoherent poses the model is unsure of? Our solution involves
modelling the pose through a simple parametric Bayesian network trained via
maximum likelihood estimation. Therefore, poses incurring a low likelihood
within our framework are out-of-distribution samples making them suitable
candidates for annotation. We also observe two useful side-outcomes: VL4Pose
in-principle yields better uncertainty estimates by unifying joint and pose
level ambiguity, as well as the unintentional but welcome ability of VL4Pose to
perform pose refinement in limited scenarios. We perform qualitative and
quantitative experiments on three datasets: MPII, LSP and ICVL, spanning human
and hand pose estimation. Finally, we note that VL4Pose is simple,
computationally inexpensive and competitive, making it suitable for challenging
tasks such as on-device active learning.
