Scene classification is a valuable classification subtask and has its own characteristics which still needs more in-depth studies. Basically, scene characteristics are distributed over the whole image, which cause the need of "seeing" comprehensive and informative regions. Previous works mainly focus on region discovery and aggregation, while rarely involves the inherent properties of CNN along with its potential ability to satisfy the requirements of scene classification. In this paper, we propose to understand scene images and the scene classification CNN models in terms of the focus area. From this new perspective, we find that large focus area is preferred in scene classification CNN models as a consequence of learning scene characteristics. Meanwhile, the analysis about existing training schemes helps us to understand the effects of focus area, and also raises the question about optimal training method for scene classification.

Deep learning-based approaches have achieved great success in the semantic segmentation [24, 43, 2, 7, 42, 3, 17, 10], thanks to a large amount of fully annotated data. However, collecting large-scale accurate pixel-level annotations can be extremely time and cost consuming [6]. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which groundtruth annotations are generated automatically [33, 34, 32]. Unfortunately, models trained purely on simulated data often fail to generalize to the real world due to thedomain shifts.

Despite such phenomenal achievement, semantic segmentation approaches still suffer from the chronic limitations caused byclass imbalance andstereotyped scene contextindatasets.