Features from multiple scales can greatly benefit the semantic edge detection
task if they are well fused. However, the prevalent semantic edge detection
methods apply a fixed weight fusion strategy where images with different
semantics are forced to share the same weights, resulting in universal fusion
weights for all images and locations regardless of their different semantics or
local context. In this work, we propose a novel dynamic feature fusion strategy
that assigns different fusion weights for different input images and locations
adaptively. This is achieved by a proposed weight learner to infer proper
fusion weights over multi-level features for each location of the feature map,
conditioned on the specific input. In this way, the heterogeneity in
contributions made by different locations of feature maps and input images can
be better considered and thus help produce more accurate and sharper edge
predictions. We show that our model with the novel dynamic feature fusion is
superior to fixed weight fusion and also the na\"ive location-invariant weight
fusion methods, via comprehensive experiments on benchmarks Cityscapes and SBD.
In particular, our method outperforms all existing well established methods and
achieves new state-of-the-art.

本文提出了一种新颖的动态特征融合策略，通过权重学习器对不同输入图像和位置适应地推断各级特征的融合权重，以更好地考虑由 Feature Map 的不同位置和输入图像产生的贡献，从而实现更准确、更清晰的边缘预测，实验证明该方法在语义边缘检测方面表现优异，达到了新的最高水平。