Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recently, significant improvement has been made on semantic object segmentation due to the development of deep convolutional neural networks (DCNNs). Training such a DCNN usually relies on a large number of images with pixel-level segmentation masks, and annotating these images is very costly in terms of both finance and human effort. ,  propose a simple to complex (STC) framework in which only image-level annotations are utilized to learn DCNNs for semantic segmentation. Specifically,  first train an initial segmentation network called Initial-DCNN with the saliency maps of simple images (i.e., those with a single category of major object(s) and clean background). These saliency maps can be automatically obtained by existing bottom-up salient object detection techniques, where no supervision information is needed. Then, a better network called Enhanced-DCNN is learned with supervision from the predicted segmentation masks of simple images based on the Initial-DCNN as ll as the image-level annotations. Finally, more pixel-level segmentation masks of complex images (two or more categories of objects with cluttered background), which are inferred by using Enhanced-DCNN and image-level annotations, are utilized as the supervision information to learn the Porful-DCNN for semantic segmentation. Our method utilizes $40$K simple images from Flickr.com and 10K complex images from PASCAL VOC for step-wisely boosting the segmentation network. Extensive experimental results on PASCAL VOC 2012 segmentation benchmark demonstrate that the proposed STC framework outperforms the state-of-the-art algorithms for akly-supervised semantic segmentation by a large margin (e.g., 10.6% over MIL-ILP-seg [1]).
['Yunchao Wei', 'Xiaodan Liang', 'Yunpeng Chen', 'Xiaohui Shen', 'Ming-Ming Cheng', 'Jiashi Feng', 'Yao Zhao', 'Shuicheng Yan']
I am writing a research paper on a idea STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation
[{"from": "human", "value": "I am writing a research paper on a idea STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recently, significant improvement has been made on semantic object segmentation due to the development of deep convolutional neural networks (DCNNs). Training such a DCNN usually relies on a large number of images with pixel-level segmentation masks, and annotating these images is very costly in terms of both finance and human effort. ,  propose a simple to complex (STC) framework in which only image-level annotations are utilized to learn DCNNs for semantic segmentation. Specifically,  first train an initial segmentation network called Initial-DCNN with the saliency maps of simple images (i.e., those with a single category of major object(s) and clean background). These saliency maps can be automatically obtained by existing bottom-up salient object detection techniques, where no supervision information is needed. Then, a better network called Enhanced-DCNN is learned with supervision from the predicted segmentation masks of simple images based on the Initial-DCNN as ll as the image-level annotations. Finally, more pixel-level segmentation masks of complex images (two or more categories of objects with cluttered background), which are inferred by using Enhanced-DCNN and image-level annotations, are utilized as the supervision information to learn the Porful-DCNN for semantic segmentation. Our method utilizes $40$K simple images from Flickr.com and 10K complex images from PASCAL VOC for step-wisely boosting the segmentation network. Extensive experimental results on PASCAL VOC 2012 segmentation benchmark demonstrate that the proposed STC framework outperforms the state-of-the-art algorithms for akly-supervised semantic segmentation by a large margin (e.g., 10.6% over MIL-ILP-seg [1])."}]