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Sketch-Guided Object Localization in Natural Images

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12351)

Abstract

We introduce a novel problem of localizing all the instances of an object (seen or unseen during training) in a natural image via sketch query. We refer to this problem as sketch-guided object localization. This problem is distinctively different from the traditional sketch-based image retrieval task where the gallery set often contains images with only one object. The sketch-guided object localization proves to be more challenging when we consider the following: (i) the sketches used as queries are abstract representations with little information on the shape and salient attributes of the object, (ii) the sketches have significant variability as they are hand-drawn by a diverse set of untrained human subjects, and (iii) there exists a domain gap between sketch queries and target natural images as these are sampled from very different data distributions. To address the problem of sketch-guided object localization, we propose a novel cross-modal attention scheme that guides the region proposal network (RPN) to generate object proposals relevant to the sketch query. These object proposals are later scored against the query to obtain final localization. Our method is effective with as little as a single sketch query. Moreover, it also generalizes well to object categories not seen during training and is effective in localizing multiple object instances present in the image. Furthermore, we extend our framework to a multi-query setting using novel feature fusion and attention fusion strategies introduced in this paper. The localization performance is evaluated on publicly available object detection benchmarks, viz. MS-COCO and PASCAL-VOC, with sketch queries obtained from ‘Quick, Draw!’. The proposed method significantly outperforms related baselines on both single-query and multi-query localization tasks.

Keywords

Sketch Cross-modal retrieval Object localization One-shot learning Attention 

Notes

Acknowledgements

The authors would like to thank the Advanced Data Management Research Group, Corporate Technologies, Siemens Technology and Services Pvt. Ltd., and Pratiksha Trust, Bengaluru, India for partly supporting this research.

Supplementary material

504443_1_En_32_MOESM1_ESM.pdf (752 kb)
Supplementary material 1 (pdf 751 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Indian Institute of ScienceBengaluruIndia
  2. 2.Indian Institute of Technology, JodhpurJodhpurIndia

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