  This paper advances the fine-grained sketch-based image retrieval (FG-SBIR)
literature by putting forward a strong baseline that overshoots prior
state-of-the-arts by ~11%. This is not via complicated design though, but by
addressing two critical issues facing the community (i) the gold standard
triplet loss does not enforce holistic latent space geometry, and (ii) there
are never enough sketches to train a high accuracy model. For the former, we
propose a simple modification to the standard triplet loss, that explicitly
enforces separation amongst photos/sketch instances. For the latter, we put
forward a novel knowledge distillation module can leverage photo data for model
training. Both modules are then plugged into a novel plug-n-playable training
paradigm that allows for more stable training. More specifically, for (i) we
employ an intra-modal triplet loss amongst sketches to bring sketches of the
same instance closer from others, and one more amongst photos to push away
different photo instances while bringing closer a structurally augmented
version of the same photo (offering a gain of ~4-6%). To tackle (ii), we first
pre-train a teacher on the large set of unlabelled photos over the
aforementioned intra-modal photo triplet loss. Then we distill the contextual
similarity present amongst the instances in the teacher's embedding space to
that in the student's embedding space, by matching the distribution over
inter-feature distances of respective samples in both embedding spaces
(delivering a further gain of ~4-5%). Apart from outperforming prior arts
significantly, our model also yields satisfactory results on generalising to
new classes. Project page: https://aneeshan95.github.io/Sketch_PVT/
