  We propose a novel framework for cross-modal zero-shot learning (ZSL) in the
context of sketch-based image retrieval (SBIR). Conventionally, the SBIR schema
mainly considers simultaneous mappings among the two image views and the
semantic side information. Therefore, it is desirable to consider fine-grained
classes mainly in the sketch domain using highly discriminative and
semantically rich feature space. However, the existing deep generative
modeling-based SBIR approaches majorly focus on bridging the gaps between the
seen and unseen classes by generating pseudo-unseen-class samples. Besides,
violating the ZSL protocol by not utilizing any unseen-class information during
training, such techniques do not pay explicit attention to modeling the
discriminative nature of the shared space. Also, we note that learning a
unified feature space for both the multi-view visual data is a tedious task
considering the significant domain difference between sketches and color
images. In this respect, as a remedy, we introduce a novel framework for
zero-shot SBIR. While we define a cross-modal triplet loss to ensure the
discriminative nature of the shared space, an innovative cross-modal attention
learning strategy is also proposed to guide feature extraction from the image
domain exploiting information from the respective sketch counterpart. In order
to preserve the semantic consistency of the shared space, we consider a graph
CNN-based module that propagates the semantic class topology to the shared
space. To ensure an improved response time during inference, we further explore
the possibility of representing the shared space in terms of hash codes.
Experimental results obtained on the benchmark TU-Berlin and the Sketchy
datasets confirm the superiority of CrossATNet in yielding state-of-the-art
results.
