Objectness-aware Semantic Segmentation

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 de convolutional 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).