Scene classification has established itself as a challenging research
problem. Compared to images of individual objects, scene images could be much
more semantically complex and abstract. Their difference mainly lies in the
level of granularity of recognition. Yet, image recognition serves as a key
pillar for the good performance of scene recognition as the knowledge attained
from object images can be used for accurate recognition of scenes. The existing
scene recognition methods only take the category label of the scene into
consideration. However, we find that the contextual information that contains
detailed local descriptions are also beneficial in allowing the scene
recognition model to be more discriminative. In this paper, we aim to improve
scene recognition using attribute and category label information encoded in
objects. Based on the complementarity of attribute and category labels, we
propose a Multi-task Attribute-Scene Recognition (MASR) network which learns a
category embedding and at the same time predicts scene attributes. Attribute
acquisition and object annotation are tedious and time consuming tasks. We
tackle the problem by proposing a partially supervised annotation strategy in
which human intervention is significantly reduced. The strategy provides a much
more cost-effective solution to real world scenarios, and requires considerably
less annotation efforts. Moreover, we re-weight the attribute predictions
considering the level of importance indicated by the object detected scores.
Using the proposed method, we efficiently annotate attribute labels for four
large-scale datasets, and systematically investigate how scene and attribute
recognition benefit from each other. The experimental results demonstrate that
MASR learns a more discriminative representation and achieves competitive
recognition performance compared to the state-of-the-art methods

本文旨在提高场景识别的准确性，通过提取对象包含的属性和分类标签信息，提出了一种多任务属性 - 场景识别（MASR）网络，采用部分监督注释策略大大降低了人工干预，从而实现了更高效的属性标注。实验结果表明，MASR 学习到了更具区分力的表示，与现有方法相比具有竞争力的识别性能。