Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval

TL;DR

This paper introduces a noise-tolerant sketch-based image retrieval method that uses a reinforcement learning stroke selector to improve retrieval accuracy and make sketching more accessible for users.

Contribution

It proposes a novel reinforcement learning-based stroke selector that filters noisy strokes, enhancing existing retrieval models and enabling more user-friendly sketch-based image retrieval.

Findings

Achieved 8%-10% improvement over standard baselines.

Reported new state-of-the-art performance.

Demonstrated the selector's plug-and-play versatility.

Abstract

Sketching enables many exciting applications, notably, image retrieval. The fear-to-sketch problem (i.e., "I can't sketch") has however proven to be fatal for its widespread adoption. This paper tackles this "fear" head on, and for the first time, proposes an auxiliary module for existing retrieval models that predominantly lets the users sketch without having to worry. We first conducted a pilot study that revealed the secret lies in the existence of noisy strokes, but not so much of the "I can't sketch". We consequently design a stroke subset selector that {detects noisy strokes, leaving only those} which make a positive contribution towards successful retrieval. Our Reinforcement Learning based formulation quantifies the importance of each stroke present in a given subset, based on the extent to which that stroke contributes to retrieval. When combined with pre-trained retrieval models as a pre-processing module, we achieve a significant gain of 8%-10% over standard baselines and in turn report new state-of-the-art performance. Last but not least, we demonstrate the selector once trained, can also be used in a plug-and-play manner to empower various sketch applications in ways that were not previously possible.