Engineering-Course
How does this work compare to ATEN?
Hi, I'm looking at both your work and ATEN. The conclusions from both your papers are very similar.
In this paper, we presented a novel detection-free Part Grouping Network to investigate instance-level human parsing, which is a more pioneering and challenging work in analyzing human in the wild. To push the research boundary of human parsing to match real-world scenarios much better, we further introduce a new large-scale (...) Experimental results on PASCAL-Person-Part [6] and our CIHP dataset demonstrate the superiority of our proposed approach, which surpasses previous methods for both semantic part segmentation and edge detection tasks, and achieves state-of-the-art performance for instance-level human parsing.
In this work, we investigate video instance-level human parsing that is a more pioneering and realistic task in analyzing human in the wild. To fill the blank of video human parsing data resources, we further introduce a large-scale (...) Experimental results on DAVIS [36] and our VIP dataset demonstrate the superiority of our proposed approach, which achieves state-of-the-art performance on both video instance-level human parsing and video segmentation tasks.
I'm wondering - which produces better accuracy, this work or ATEN? Considering that both claim "more pioneering", "demonstrate the superiority of our proposed approach", and "achieve state-of-the-art", can you help explain the differences? I'm not clear which I should use. Thanks!
For difference, PGN is totally a framework for parsing images while ATEN aims at utilizing temporal information for video parsing. For frame-level accuracy, PGN is better while for sequence input, ATEN is state-of-the-art.
