Occlusion-shared and Feature-separated Network for Occlusion Relationship Reasoning

August 16, 2019 ยท Entered Twilight ยท ๐Ÿ› IEEE International Conference on Computer Vision

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Authors Rui Lu, Feng Xue, Menghan Zhou, Anlong Ming, Yu Zhou arXiv ID 1908.05898 Category cs.CV: Computer Vision Cross-listed eess.IV Citations 34 Venue IEEE International Conference on Computer Vision Repository https://github.com/buptlr/OFNet โญ 18 Last Checked 1 month ago
Abstract
Occlusion relationship reasoning demands closed contour to express the object, and orientation of each contour pixel to describe the order relationship between objects. Current CNN-based methods neglect two critical issues of the task: (1) simultaneous existence of the relevance and distinction for the two elements, i.e, occlusion edge and occlusion orientation; and (2) inadequate exploration to the orientation features. For the reasons above, we propose the Occlusion-shared and Feature-separated Network (OFNet). On one hand, considering the relevance between edge and orientation, two sub-networks are designed to share the occlusion cue. On the other hand, the whole network is split into two paths to learn the high-level semantic features separately. Moreover, a contextual feature for orientation prediction is extracted, which represents the bilateral cue of the foreground and background areas. The bilateral cue is then fused with the occlusion cue to precisely locate the object regions. Finally, a stripe convolution is designed to further aggregate features from surrounding scenes of the occlusion edge. The proposed OFNet remarkably advances the state-of-the-art approaches on PIOD and BSDS ownership dataset. The source code is available at https://github.com/buptlr/OFNet.
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