STDnet: Exploiting high resolution feature maps for small object detection-Reference-Cited by-同舟云学术

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STDnet: Exploiting high resolution feature maps for small object detection

Published:2020-05 Issue: Volume:91 Page:103615
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Bosquet Brais,Mucientes Manuel,Brea Víctor M.

Funder

Gradiant, Spain

Spanish Ministry of Economy and Competitiveness

Galician Ministry of Education, Culture and Universities, Spain

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference53 articles.

1. Bai, Y., Zhang, Y., Ding, M., Ghanem, B., 2018. Finding tiny faces in the wild with generative adversarial network. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR, pp. 21–30.

2. Bell, S., Lawrence Zitnick, C., Bala, K., Girshick, R., 2016. Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR, pp. 2874–2883.

3. Cai, Z., Fan, Q., Feris, R., Vasconcelos, N., 2016. A unified multi-scale deep convolutional neural network for fast object detection. In: European Conference on Computer Vision. ECCV, pp. 354–370.

4. Carreira, J., Zisserman, A., 2017. Quo vadis, action recognition? A new model and the kinetics dataset. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR, pp. 4724–4733.

5. R-FCN: Object detection via region-based fully convolutional networks;Dai,2016

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