  The human visual system is remarkable in learning new visual concepts from
just a few examples. This is precisely the goal behind few-shot class
incremental learning (FSCIL), where the emphasis is additionally placed on
ensuring the model does not suffer from "forgetting". In this paper, we push
the boundary further for FSCIL by addressing two key questions that bottleneck
its ubiquitous application (i) can the model learn from diverse modalities
other than just photo (as humans do), and (ii) what if photos are not readily
accessible (due to ethical and privacy constraints). Our key innovation lies in
advocating the use of sketches as a new modality for class support. The product
is a "Doodle It Yourself" (DIY) FSCIL framework where the users can freely
sketch a few examples of a novel class for the model to learn to recognize
photos of that class. For that, we present a framework that infuses (i)
gradient consensus for domain invariant learning, (ii) knowledge distillation
for preserving old class information, and (iii) graph attention networks for
message passing between old and novel classes. We experimentally show that
sketches are better class support than text in the context of FSCIL, echoing
findings elsewhere in the sketching literature.
