Multi-scale recognition with DAG-CNNs
We explore multi-scale convolutional neural nets (CNNs)
for image classification. Contemporary approaches extract
features from a single output layer. By extracting features
from multiple layers, one can simultaneously reason about
high, mid, and low-level features during classification. The
resulting multi-scale architecture can itself be seen as a
feed-forward model that is structured as a directed acyclic
graph (DAG-CNNs). We use DAG-CNNs to learn a set of
multiscale features that can be effectively shared between
coarse and fine-grained classification tasks. While finetuning
such models helps performance, we show that even
“off-the-self” multiscale features perform quite well. We
present extensive analysis and demonstrate state-of-the-art
classification performance on three standard scene benchmarks
(SUN397, MIT67, and Scene15). In terms of the
heavily benchmarked MIT67 and Scene15 datasets, our results
reduce the lowest previously-reported error by 23.9%
and 9.5%, respectively.
Download: pdf
Text Reference
Songfan Yang and Deva Ramanan. Multi-scale recognition with dag-cnns. In IEEE International Conference on Computer Vision. 2015.BibTeX Reference
@INPROCEEDINGS{YangR_ICCV_2015,author = "Yang, Songfan and Ramanan, Deva",
booktitle = "IEEE International Conference on Computer Vision",
title = "Multi-scale recognition with DAG-CNNs",
year = "2015",
tag = "object_recognition"
}