Review for NeurIPS paper: Attribute Prototype Network for Zero-Shot Learning

Weaknesses: Novelty 1- The proposed model is mainly building on previous ideas: [8] for learning prototypes, [15] for decorrelation and sharing, [52] for localization compactness, and [7] for score calibration. This renders the technical novelty to be somewhat limited. Nonetheless, I find the employment of these ideas together for attribute localization and ZSL is quite interesting and seems to lead to consistent good performance. Model: 2- It seems that the model uses continuous attributes. This type of attributes is usually obtained averaging the image-level binary attributes for each class which is expensive to obtain.