Short videos have many applications on fashion trends, hot spots, street interviews, public education, and creative advertising. We propose an Edge-Aware Network (EANet) that uses edge information to refine the segmentation edge. And experiments show our proposed EANet boots up the facial parsing results. We also use post-process like grab cut to refine and merge the parsing results.\\%DIF > accuracy in the Short-video Face Parsing track of the 3rd Person in Context (PIC) Workshop and Challenge, ranked the third place.
This is a short technical report introducing the solution of Team Rat for Short-video Parsing Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR 2021. In this report, we propose an Edge-Aware Network (EANet) that uses edge information to refine the segmentation edge. To further obtain the finer edge results, we introduce edge attention loss that only compute cross entropy on the edges, it can effectively reduce the classification error around edge and get more smooth boundary. Benefiting from the edge information and edge attention loss, the proposed EANet achieves 86.16\\%DIF > accuracy in the Short-video Face Parsing track of the 3rd Person in Context (PIC) Workshop and Challenge, ranked the third place.
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Short videos have many applications on fashion trends, hot spots, street interviews, public education, and creative advertising. We
This is a short technical report introducing the solution of Team Rat for Short-video Parsing Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR 2021. In this report, we
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And experiments show our proposed EANet boots up the facial parsing results. We also use post-process like grab cut to refine and merge the parsing results.
To further obtain the finer edge results, we introduce edge attention loss that only compute cross entropy on the edges, it can effectively reduce the classification error around edge and get more smooth boundary. Benefiting from the edge information and edge attention loss, the proposed EANet achieves 86.16