Cost-Effective Object Detection: Active Sample Mining with Switchable Selection Criteria

View Researcher II's Other Codes

Cost-Effective Object Detection: Active Sample Mining with Switchable Selection Criteria

Disclaimer: “The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).”

Please contact us in case of a broken link from here

Authors Keze Wang, Liang Lin, Xiaopeng Yan, Ziliang Chen, Dongyu Zhang, and Lei Zhang
Journal/Conference Name IEEE Transactions on Neural Networks and Learning Systems
Paper Category
Paper Abstract Though quite challenging, the training of object detectors using large-scale unlabeled or partially labeled datasets has attracted increasing interests from researchers due to its fundamental importance for applications of neural networks and learning systems. To address this problem, many active learning (AL) methods have been proposed that employ up-to-date detectors to retrieve representative minority samples according to predefined confidence or uncertainty thresholds. However, these AL methods cause the detectors to ignore the remaining majority samples (i.e., those with low uncertainty or high prediction confidence). In this work, by developing a principled active sample mining (ASM) framework, we demonstrate that cost-effectively mining samples from these unlabeled majority data is key to training more powerful object detectors while minimizing user effort. Specifically, our ASM framework involves a selectively switchable sample selection mechanism for determining whether an unlabeled sample should be manually annotated via AL or automatically pseudo-labeled via a novel self-learning process. The proposed process can be compatible with mini-batch based training (i.e., using a batch of unlabeled or partially labeled data as a one-time input) for object detection. Extensive experiments on two public benchmarks clearly demonstrate that our ASM framework can achieve performance comparable to that of alternative methods but with significantly fewer annotations.
Date of publication 2018
Code Programming Language Pytorch

Copyright Researcher II 2021