Few-Shot Adaptive Detection of Objects of Concern Using Generative Models with Negative Retraining

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Detecting objects which we are interested in, Objects of Concern (OoC), is nowadays attracting attention. In aviation and transport, it is important to robustly detect OoC in security images. OoC are rare, differ from typical samples, and may be unknown during training. Most OoC detection methods need to be trained on large datasets to achieve good performance and have limited real-world generalization ability. A large variety of samples is needed, and it is expensive to collect and label large datasets due to the rarity of OoC. To address such limitations, we propose the negative REtraining with Few-shots Generative Adversarial Network (REFGAN) for detecting OoC. REFGAN aims at automatically identifying OoC by learning from Objects of No Concern (OoNC) and OoC. Our methodology comprises learning a prior using OoNC, and few-shot adaptation using the Few-Shot OoC (FSOoC). We propose a methodology to robustly perform few-shot adaptive detection of OoC using GANs and learned distribution boundaries. The evaluation of REFGAN on the Baggage SIXray dataset shows that when FSOoC are used, our model outperforms the prior improving OoC detection, and outperforms recent benchmarks by approximately 6.3% in mean values. REFGAN using few-shots of 80 samples shows a robust comparable performance compared to REFGAN using all the samples for retraining and model adaptation. REFGAN can detect unknown OoC and its evaluation on SIXray and CIFAR-10 shows robustness against the number of few-shot samples of OoC. REFGAN on CIFAR-10 outperforms benchmarks by approximately 16% using few-shots of 80 and of 10 samples.
Original languageEnglish
Title of host publication2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)
PublisherIEEE
Pages8
ISBN (Electronic)978-1-6654-0898-1
DOIs
Publication statusE-pub ahead of print - 21 Dec 2021
EventThe 33rd IEEE International Conference on Tools with Artificial Intelligence - Virtual
Duration: 1 Nov 20213 Nov 2021
https://ictai.computer.org/

Conference

ConferenceThe 33rd IEEE International Conference on Tools with Artificial Intelligence
Abbreviated titleICTAI
Period1/11/213/11/21
Internet address

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