Training object class detectors with click supervision

Abstract

Training object class detectors typically requires a large set of images with objects annotated by bounding boxes. However, manually drawing bounding boxes is very time consuming. In this paper we greatly reduce annotation time by proposing center-click annotations: we ask annotators to click on the center of an imaginary bounding box which tightly encloses the object instance. We then incorporate these clicks into existing Multiple Instance Learning techniques for weakly supervised object localization, to jointly localize object bounding boxes over all training images. Extensive experiments on PASCAL VOC 2007 and MS COCO show that: (1) our scheme delivers high-quality detectors, performing substantially better than those produced by weakly supervised techniques, with a modest extra annotation effort; (2) these detectors in fact perform in a range close to those trained from manually drawn bounding boxes; (3) as the center-click task is very fast, our scheme reduces total annotation time by 9x to 18x.

CVPR 2017

References in corpus (6)

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• Discovering Attribute Shades of Meaning with the Crowd
• ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
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Cited by in corpus (10)

• Deep Extreme Cut: From Extreme Points to Object Segmentation
• Deep Learning Based Automatic Video Annotation Tool for Self-Driving Car
• Learning Intelligent Dialogs for Bounding Box Annotation
• ClickBAIT-v2: Training an Object Detector in Real-Time
• LOST: A flexible framework for semi-automatic image annotation
• A Taught-Obesrve-Ask (TOA) Method for Object Detection with Critical Supervision
• One-Click Annotation with Guided Hierarchical Object Detection
• A Multiclass Multiple Instance Learning Method with Exact Likelihood
• Autonomous Curiosity for Real-Time Training Onboard Robotic Agents
• Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation