  We present a novel method for multi image domain and multi-landmark
definition learning for small dataset facial localization. Training a small
dataset alongside a large(r) dataset helps with robust learning for the former,
and provides a universal mechanism for facial landmark localization for new
and/or smaller standard datasets. To this end, we propose a Vision Transformer
encoder with a novel decoder with a definition agnostic shared landmark
semantic group structured prior, that is learnt, as we train on more than one
dataset concurrently. Due to our novel definition agnostic group prior the
datasets may vary in landmark definitions and domains. During the decoder stage
we use cross- and self-attention, whose output is later fed into
domain/definition specific heads that minimize a Laplacian-log-likelihood loss.
We achieve state-of-the-art performance on standard landmark localization
datasets such as COFW and WFLW, when trained with a bigger dataset. We also
show state-of-the-art performance on several varied image domain small datasets
for animals, caricatures, and facial portrait paintings. Further, we contribute
a small dataset (150 images) of pareidolias to show efficacy of our method.
Finally, we provide several analysis and ablation studies to justify our
claims.
