  The significance of multi-scale features has been gradually recognized by the
edge detection community. However, the fusion of multi-scale features increases
the complexity of the model, which is not friendly to practical application. In
this work, we propose a Compact Twice Fusion Network (CTFN) to fully integrate
multi-scale features while maintaining the compactness of the model. CTFN
includes two lightweight multi-scale feature fusion modules: a Semantic
Enhancement Module (SEM) that can utilize the semantic information contained in
coarse-scale features to guide the learning of fine-scale features, and a
Pseudo Pixel-level Weighting (PPW) module that aggregate the complementary
merits of multi-scale features by assigning weights to all features.
Notwithstanding all this, the interference of texture noise makes the correct
classification of some pixels still a challenge. For these hard samples, we
propose a novel loss function, coined Dynamic Focal Loss, which reshapes the
standard cross-entropy loss and dynamically adjusts the weights to correct the
distribution of hard samples. We evaluate our method on three datasets, i.e.,
BSDS500, NYUDv2, and BIPEDv2. Compared with state-of-the-art methods, CTFN
achieves competitive accuracy with less parameters and computational cost.
Apart from the backbone, CTFN requires only 0.1M additional parameters, which
reduces its computation cost to just 60% of other state-of-the-art methods. The
codes are available at https://github.com/Li-yachuan/CTFN-pytorch-master.
