Elevating All Zero-Shot Sketch-Based Image Retrieval Through Multimodal Prompt Learning

TL;DR

This paper introduces SpLIP, a multi-modal prompt learning approach leveraging CLIP's visual and textual capabilities to improve zero-shot sketch-based image retrieval across various challenging settings.

Contribution

SpLIP is the first multi-modal prompt learning scheme with bi-directional prompt sharing for SBIR, enhancing cross-modal alignment and generalization.

Findings

SpLIP outperforms existing methods on multiple SBIR benchmarks.

The bi-directional prompt sharing improves semantic alignment between sketches and photos.

Adaptive margin and conditional cross-modal jigsaw strategies further boost retrieval accuracy.

Abstract

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/ .