UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation

Siddhesh Khandelwal; Raghav Goyal; Leonid Sigal

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arXiv · 2006.07502

UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation

Siddhesh Khandelwal·Raghav Goyal·Leonid Sigal

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Abstract

Methods for object detection and segmentation rely on large scale instance-level annotations for training, which are difficult and time-consuming to collect. Efforts to alleviate this look at varying degrees and quality of supervision. Weakly-supervised approaches draw on image-level labels to build detectors/segmentors, while zero/few-shot methods assume abundant instance-level data for a set of base classes, and none to a few examples for novel classes. This taxonomy has largely siloed algorithmic designs. In this work, we aim to bridge this divide by proposing an intuitive and unified semi-supervised model that is applicable to a range of supervision: from zero to a few instance-level samples per novel class. For base classes, our model learns a mapping from weakly-supervised to fully-supervised detectors/segmentors. By learning and leveraging visual and lingual similarities between the novel and base classes, we transfer those mappings to obtain detectors/segmentors for novel classes; refining them with a few novel class instance-level annotated samples, if available. The overall model is end-to-end trainable and highly flexible. Through extensive experiments on MS-COCO and Pascal VOC benchmark datasets we show improved performance in a variety of settings.

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BibTeX RIS

Siddhesh Khandelwal, Raghav Goyal, Leonid Sigal. 2020-06-12. UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation. https://arxiv.org/abs/2006.07502

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