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Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem ofretrieving a particular photo in a given query sketch. However, its widespreadapplicability is limited by the fact that it is difficult to draw a completesketch for most people, and the drawing process often takes time. In thisstudy, we aim to retrieve the target photo with the least number of strokespossible (incomplete sketch), named on-the-fly FG-SBIR (Bhunia et al. 2020),which starts retrieving at each stroke as soon as the drawing begins. Weconsider that there is a significant correlation among these incompletesketches in the sketch drawing episode of each photo. To learn more efficientjoint embedding space shared between the photo and its incomplete sketches, wepropose a multi-granularity association learning framework that furtheroptimizes the embedding space of all incomplete sketches. Specifically, basedon the integrity of the sketch, we can divide a complete sketch episode intoseveral stages, each of which corresponds to a simple linear mapping layer.Moreover, our framework guides the vector space representation of the currentsketch to approximate that of its later sketches to realize the retrievalperformance of the sketch with fewer strokes to approach that of the sketchwith more strokes. In the experiments, we proposed more realistic challenges,and our method achieved superior early retrieval efficiency over thestate-of-the-art methods and alternative baselines on two publicly availablefine-grained sketch retrieval datasets.

Multi-granularity Association Learning Framework for on-the-fly Fine-Grained Sketch-based Image Retrieval