A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf
.
Scene Recognition with CNNs: Objects, Scales and Dataset Bias
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Since scenes are composed in part of objects, accurate recognition of scenes requires knowledge about both scenes and objects. In this paper we address two related problems: 1) scale induced dataset bias in multi-scale convolutional neural network (CNN) architectures, and 2) how to combine effectively scene-centric and object-centric knowledge (i.e. Places and ImageNet) in CNNs. An earlier attempt, Hybrid-CNN, showed that incorporating ImageNet did not help much. Here we propose an alternative
doi:10.1109/cvpr.2016.68
dblp:conf/cvpr/HerranzJL16
fatcat:ijjyj5oz3jf77auw72vyyh2nf4