Active learning for deep detection neural networks

Hamed H. Aghdam, Abel Gonzalez-Garcia, Antonio Lopez, Joost Weijer

Producció científica: Contribució a una revistaArticleRecercaAvaluat per experts

94 Cites (Scopus)
3 Descàrregues (Pure)

Resum

The cost of drawing object bounding boxes (i.e. labeling) for millions of images is prohibitively high. For instance, labeling pedestrians in a regular urban image could take 35 seconds on average. Active learning aims to reduce the cost of labeling by selecting only those images that are informative to improve the detection network accuracy. In this paper, we propose a method to perform active learning of object detectors based on convolutional neural networks. We propose a new image-level scoring process to rank unlabeled images for their automatic selection, which clearly outperforms classical scores. The proposed method can be applied to videos and sets of still images. In the former case, temporal selection rules can complement our scoring process. As a relevant use case, we extensively study the performance of our method on the task of pedestrian detection. Overall, the experiments show that the proposed method performs better than random selection.

Idioma originalEnglish
Pàgines (de-a)3671-3679
Nombre de pàgines9
RevistaIEEE International Conference on Computer Vision
DOIs
Estat de la publicacióPublicada - d’oct. 2019

Fingerprint

Navegar pels temes de recerca de 'Active learning for deep detection neural networks'. Junts formen un fingerprint únic.

Com citar-ho