Paper:
Learning Deep Features for Scene Recognition using Places Database
Scene recognition is one of the hallmark tasks of computer vision, allowing definition of a context for object recognition. Whereas the tremendous recent progress in object recognition tasks is due to the availability of large datasets like ImageNet and the rise of Convolutional Neural Networks (CNNs) for learning high-level features, performance at scene recognition has not attained the same level of success. This may be because current deep features trained from ImageNet are not competitive enough for such tasks. Here, we introduce a new scene-centric database called Places with over 7 million labeled pictures of scenes. We propose new methods to compare the density and diversity of image datasets and show that Places is as dense as other scene datasets and has more diversity. Using CNN, we learn deep features for scene recognition tasks, and establish new state-of-the-art results on several scene-centric datasets. A visualization of the CNN layers' responses allows us to show differences in the internal representations of object-centric and scene-centric networks.

Review:
This paper introduces a scene-centric database called PLACES with more than 6 million of labeled pictures for scene recognition. The purpose of building such a database is to complement to ImageNet which is an object-centric database, and improve the performance of scene recognition by learning deep features using PLACES. The database was built by scraping search engines with composite queries and utilizing Amazon Mechanical Turk (AMT) for validation. To compare the density and diversity of PLACES with existing databases such as SUN and ImageNet, the authors proposed two metrics and again utilized AMT to get the measures and show the advantages of PLACES. The authors also compared the features learned from PLACES and from ImageNet, and showed the two databases are complementary to each other. In particular, PLACES can result in to better performance for scene recognition and ImageNet better for object recognition.Strong points of this this work:1. A solid work on dataset building.2. Detailed and convincing better result than existing scene databases. Shows the power of data.Minor concerns and comments:1. Limited algorithm contribution.2. For density, nearest neighbors are obtained based on the GIST feature. Though this setting is the same for all the databases, I wonder if depending on a particular feature will lead to any bias for the density metric.3. For diversity, judgment can be quite difficult if judges are presented with two random images, as it doesn’t make sense to judge the similarity between two unrelated random images. I wonder if the authors have similar observations in their AMT experiment.4. As PLACES and ImageNet are two complementary databases, how about to mix them together to train a model that is good for both object and scene recognition?