- It is claimed that the proposed method aims to discrminatively localize the sounding objects from their mixed sound without any manual annotations. However, the method aslo aims to do class-aware localization. As shown in Figure 4, the object categories are labeled for the localized regions for the proposed method. It is unclear to this reviewer whether the labels there are only for illustrative purposes? - Even the proposed method doesn't rely on any class labels, it needs the number of categories of potential sound sources in the data to build the object dictionary. - Though the performance of method is pretty good especially in Table 2, the novelty/contribution of the method is somewhat incremental. The main contribution of the work is a new network design drawing inspirations from prior work for the sound source localization task. - The method assumes single source videos are available to train in the first stage, which is also a strong assumption even though class labels are not used. Most in-the-wild videos are noisy and multi-source. It would be desired to have some analysis to show how robust the system is to noise in videos or how the system can learn without clean single source videos to build the object dictionary.
- Though the performance of method is pretty good especially in Table 2, the novelty/contribution of the method is somewhat incremental. The main contribution of the work is a new network design drawing inspirations from prior work for the sound source localization task.