  Pose estimation is the task of locating keypoints for an object of interest
in an image. Animal Pose estimation is more challenging than estimating human
pose due to high inter and intra class variability in animals. Existing works
solve this problem for a fixed set of predefined animal categories. Models
trained on such sets usually do not work well with new animal categories.
Retraining the model on new categories makes the model overfit and leads to
catastrophic forgetting. Thus, in this work, we propose a novel problem of
"Incremental Learning for Animal Pose Estimation". Our method uses an exemplar
memory, sampled using Determinantal Point Processes (DPP) to continually adapt
to new animal categories without forgetting the old ones. We further propose a
new variant of k-DPP that uses RBF kernel (termed as "RBF k-DPP") which gives
more gain in performance over traditional k-DPP. Due to memory constraints, the
limited number of exemplars along with new class data can lead to class
imbalance. We mitigate it by performing image warping as an augmentation
technique. This helps in crafting diverse poses, which reduces overfitting and
yields further improvement in performance. The efficacy of our proposed
approach is demonstrated via extensive experiments and ablations where we
obtain significant improvements over state-of-the-art baseline methods.
