VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation

by   Megh Shukla, et al.

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.


page 2

page 8

page 9

page 19


Active Learning for Bayesian 3D Hand Pose Estimation

We propose a Bayesian approximation to a deep learning architecture for ...

Active Learning with Pseudo-Labels for Multi-View 3D Pose Estimation

Pose estimation of the human body/hand is a fundamental problem in compu...

Explaining the Ambiguity of Object Detection and 6D Pose from Visual Data

3D object detection and pose estimation from a single image are two inhe...

A Mathematical Analysis of Learning Loss for Active Learning in Regression

Active learning continues to remain significant in the industry since it...

EGL++: Extending Expected Gradient Length to Active Learning for Human Pose Estimation

State of the art human pose estimation models continue to rely on large ...

Deep Learning-based Face Pose Recovery

Facial pose estimation has gained a lot of attentions in many practical ...

An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis

Polynomial chaos expansions (PCE) have seen widespread use in the contex...

Please sign up or login with your details

Forgot password? Click here to reset