Convolutional Neural Networks (CNNs) have made remarkable progress on scene
recognition, partially due to these recent large-scale scene datasets, such as
the Places and Places2. Scene categories are often defined by multi-level
information, including local objects, global layout, and background
environment, thus leading to large intra-class variations. In addition, with
the increasing number of scene categories, label ambiguity has become another
crucial issue in large-scale classification. This paper focuses on large-scale
scene recognition and makes two major contributions to tackle these issues.
First, we propose a multi-resolution CNN architecture that captures visual
content and structure at multiple levels. The multi-resolution CNNs are
composed of coarse resolution CNNs and fine resolution CNNs, which are
complementary to each other. Second, we design two knowledge guided
disambiguation techniques to deal with the problem of label ambiguity. (i) We
exploit the knowledge from the confusion matrix computed on validation data to
merge ambiguous classes into a super category. (ii) We utilize the knowledge of
extra networks to produce a soft label for each image. Then the super
categories or soft labels are employed to guide CNN training on the Places2. We
conduct extensive experiments on three large-scale image datasets (ImageNet,
Places, and Places2), demonstrating the effectiveness of our approach.
Furthermore, our method takes part in two major scene recognition challenges,
and achieves the second place at the Places2 challenge in ILSVRC 2015, and the
first place at the LSUN challenge in CVPR 2016. Finally, we directly test the
learned representations on other scene benchmarks, and obtain the new
state-of-the-art results on the MIT Indoor67 (86.7\%) and SUN397 (72.0\%). We
release the code and models
at~https://github.com/wanglimin/MRCNN-Scene-Recognition.

本文提出了一种多分辨率卷积神经网络架构来捕捉多级场景信息，并设计了两种知识引导的消歧技术来解决标签模糊性问题，实验结果证明该方法在三个大型图像数据集上获得了最先进的结果，并在两个主要的场景识别挑战中获得了第二和第一的成绩。