A First Look Towards One-Shot Object Detection with SPOT for Data-Efficient Learning

In this work we discuss One-Shot Object Detection, a challenging task of detecting novel objects in a target scene using a single reference image called a query. To address this challenge we introduce SPOT (Surfacing POsitions using Transformers), a novel transformer based end-to-end architecture which uses synergy between the provided query and target images using a learnable Robust Feature Matching module to emphasize the features of targets based on visual cues from the query. We curate LocateDS - a large dataset of query-target pairs from open-source logo and annotated product images containing pictograms, which are better candidates for the one-shot detection problem. Initial results on this dataset show that our model performs significantly better than the current state-of-the-art. We also extend SPOT to a novel real-life downstream task of Intelligent Sample Selection from a domain with very different distribution.