The paper proposes LSD-NET, an active vision method for object classification.  In the proposed method, based on a given view of an object, the algorithm can decide to either classify the object or to take a discrete action step which will move the camera in order to acquire a different view of the object.  Following this procedure the algorithm iteratively moves around the object until reaching a maximum number of allowed moves or until a object view favorable for classification is reached. The main contribution of the paper is a hierarchical action space that distinguishes between camera-movement actions and classification actions.  At the top-level of the hierarchy, the algorithm decides whether to perform a movement or a classification -type action.  At the lower-level, the algorithm either assign a specific class label (for the case of classification actions) or performs a camera movement (for the case of camera-movement actions).  This hierarchical action space results in reduced bias towards classification actions. Strong Points - The content is clear and easy to follow. - The proposed method achieves competitive performance w.r.t. existing work. Weak Points- Some aspects of the proposed method could have been evaluated better. - A deeper evaluation/analysis of the proposed method is missing. Overall the proposed method is sound and the paper has a good flow and is easy to follow.  The proposed method achieves competitive results,  and up to some extent, shows why it is important to have the proposed hierarchical action space. My main concerns with this manuscript are the following: In some of the tables a LSTM variant? of the proposed method is mentioned.  However it is never introduced properly in the text.  Can you indicate how this LSTM-based method differs from the proposed method? At the end of Section 5.2 the manuscript states: "In comparison to other methods, our method is agnostic of the starting point i.e. it can start randomly on any image and it would get similar testing accuracies."  This suggests that the method has been evaluated over different trials considering different random initializations.  However, this is unclear based on the evaluation protocol presented in Section 5.  If this is not the case, perhaps this is an experiment that should be conducted. In Section 3.2 it is mentioned that different from typical deep reinforcement learning methods, the proposed method uses a deeper AlexNet-like network.  In this context, it would be useful to drop a comment on the computation costs added in training/testing by this deeper model. Table 3 shows the number of correctly and wrongly classified objects as a function  of the number of steps taken.  Here we can notice that around 50% of the objects are in the step 1 and 12,  which as correctly indicated by the manuscript, suggests that movement does not help for those cases.  Would it be possible to have more class-specific (or classes grouped into intermediate categories) visualization of the results?  This would provide a better insight of what is going on and when exactly actions related to camera movements really help to get better classification performance.  On the presentation side, I would recommend displaying the content of Table 3 in a plot.  This may display the trends more clearly.  Moreover, I would recommend to visualize the classification accuracy as a function of the step taken by the method.  In this regard, a deeper analysis of the effect of the proposed hierarchical action space is a must. I would encourage the authors to address the concerns raised on my review.