Towards Fine-grained Sampling for Active Learning in Object Detection

Vikas Desai, Sai and Balasubramanian, Vineeth N (2020) Towards Fine-grained Sampling for Active Learning in Object Detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 14 June 2020 - 19 June 2020.

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We study the problem of using active learning to reduce annotation effort in training object detectors. Existing efforts in this space ignore the fact that image annotation costs are variable, depending on the number of objects present in a single image. In this regard, we examine a fine-grained sampling based approach for active learning in object detection. Over an unlabeled pool of images, our method aims to selectively pick the most informative subset of bounding boxes (as opposed to full images) to query an annotator. We measure annotation efforts in terms of the number of ground truth bounding boxes obtained. We study the effects of our method on the Feature Pyramid Network and RetinaNet models, and show promising savings in labeling effort to obtain good detection performance.

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IITH Creators:
IITH CreatorsORCiD
Balasubramanian, Vineeth NUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Active Learning; Detection performance; Feature pyramid; Fine grained; Ground truth; Sampling-based; Single images; Training objects;Computer vision; Object recognition
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 31 Jul 2021 11:17
Last Modified: 31 Jul 2021 11:17
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