  We address the challenges inherent in sketch-based image retrieval (SBIR)
across various settings, including zero-shot SBIR, generalized zero-shot SBIR,
and fine-grained zero-shot SBIR, by leveraging the vision-language foundation
model CLIP. While recent endeavors have employed CLIP to enhance SBIR, these
approaches predominantly follow uni-modal prompt processing and overlook to
exploit CLIP's integrated visual and textual capabilities fully. To bridge this
gap, we introduce SpLIP, a novel multi-modal prompt learning scheme designed to
operate effectively with frozen CLIP backbones. We diverge from existing
multi-modal prompting methods that treat visual and textual prompts
independently or integrate them in a limited fashion, leading to suboptimal
generalization. SpLIP implements a bi-directional prompt-sharing strategy that
enables mutual knowledge exchange between CLIP's visual and textual encoders,
fostering a more cohesive and synergistic prompt processing mechanism that
significantly reduces the semantic gap between the sketch and photo embeddings.
In addition to pioneering multi-modal prompt learning, we propose two
innovative strategies for further refining the embedding space. The first is an
adaptive margin generation for the sketch-photo triplet loss, regulated by
CLIP's class textual embeddings. The second introduces a novel task, termed
conditional cross-modal jigsaw, aimed at enhancing fine-grained sketch-photo
alignment by implicitly modeling sketches' viable patch arrangement using
knowledge of unshuffled photos. Our comprehensive experimental evaluations
across multiple benchmarks demonstrate the superior performance of SpLIP in all
three SBIR scenarios. Project page: https://mainaksingha01.github.io/SpLIP/ .
