Objectness-aware Semantic Segmentation

	Objectness-aware Semantic Segmentation
	Yuhang Wang; Jing Liu; Yong Li; Junjie Yan; Hanqing Lu
	2016
会议名称	ACM Multimedia
会议录名称	Proceedings of the 2016 ACM on Multimedia Conference
会议日期	October 15 – 19 , 2016
会议地点	Amsterdam, Netherlands
摘要	Recent advances in semantic segmentation are driven by the success of fully convolutional neural network (FCN). However, the coarse label map from the network and the object discrimination ability for semantic segmentation weaken the performance of those FCN-based models. To address these issues, we propose an objectness-aware semantic segmentation framework (OA-Seg) by jointly learning an object proposal network (OPN) and a lightweight deconvolutional neural network (Light-DCNN). First, OPN is learned based on a fully convolutional architecture to simultaneously predict object bounding boxes and their objectness scores. Second, we design a Light-DCNN to provide a finer upsampling way than FCN. The Light-DCNN is constructed with convolutional layers in VGG-net and their mirrored deconvolutional structure, where all fully-connected layers are removed. And hierarchical classification layers are added to multi-scale deconvolutional features to introduce more contextual information for pixel-wise label prediction. Compared with previous works, our approach performs an obvious decrease on model size and convergence time. Thorough evaluations are performed on the PASCAL VOC 2012 benchmark, and our model yields impressive results on its validation data (70.3% mean IoU) and test data (74.1% mean IoU).
关键词	Deconvolutional Neural Network Semantic Segmentation
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13440
专题	紫东太初大模型研究中心_图像与视频分析
通讯作者	Jing Liu
作者单位	中国科学院自动化研究所
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yuhang Wang,Jing Liu,Yong Li,et al. Objectness-aware Semantic Segmentation[C],2016.

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