ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Sketch-Guided Object Localization in Natural Images

Tripathi, A and Dani, RR and Mishra, A and Chakraborty, A (2020) Sketch-Guided Object Localization in Natural Images. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 23 - 28 August 2020, Glasgow, pp. 532-547.

ECCV 2020.pdf - Published Version

Download (1MB) | Preview
Official URL: https://doi.org/10.1007/978-3-030-58539-6_32


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.

Item Type: Conference Paper
Publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer Science and Business Media Deutschland GmbH
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Benchmarking; Computer vision; Image retrieval; Object detection; Object recognition, Abstract representation; Data distribution; Fusion strategies; Localization performance; Multiple objects; Object categories; Object localization; Sketch-based image retrievals, Drawing (graphics)
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 23 Jan 2023 10:17
Last Modified: 23 Jan 2023 10:17
URI: https://eprints.iisc.ac.in/id/eprint/79266

Actions (login required)

View Item View Item