Extended " T able 2" in submitted paper. Extended " T able 3" in submitted paper. We thank reviewers for their comments, and will carefully revise paper considering these comments. Q1 (R1): References and comparison with a baseline that learns embeddings only through a standard convnet. In Tab.2 of this rebuttal, the state-of-the-art method of AISI [7] also depends on We will give more details of these compared methods in paper for clarity.

Our experimental evaluations demonstrate that this simple modification significantly improves the quality of localization maps on both the P ASCAL VOC 2012 and MS COCO 2014 datasets, exhibiting a new state-of-the-art performance for weakly supervised semantic segmentation.

Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminative region of the target object. We interpret this phenomenon using the information bottleneck principle: the final layer of a deep neural network, activated by the sigmoid or softmax activation functions, causes an information bottleneck, and as a result, only a subset of the task-relevant information is passed on to the output. We first support this argument through a simulated toy experiment and then propose a method to reduce the information bottleneck by removing the last activation function. In addition, we introduce a new pooling method that further encourages the transmission of information from non-discriminative regions to the classification. Our experimental evaluations demonstrate that this simple modification significantly improves the quality of localization maps on both the PASCAL VOC 2012 and MS COCO 2014 datasets, exhibiting a new state-of-the-art performance for weakly supervised semantic segmentation.

Weakly-supervised segmentation with label-efficient sparse annotations has attracted increasing research attention to reduce the cost of laborious pixel-wise labeling process, while the pairwise affinity modeling techniques play an essential role in this task. Most of the existing approaches focus on using the local appearance kernel to model the neighboring pairwise potentials.

However, the existing WSSS studies have focused on a single domain, i .