ECT: Fine-grained Edge Detection with Learned Cause Tokens
Shaocong Xu Xiaoxue Chen Yuhang Zheng Guyue Zhou Yurong Chen Hongbin Zha Hao Zhao
In this study, we tackle the challenging fine-grained edge detection task,
which refers to predicting specific edges caused by reflectance, illumination,
normal, and depth changes, respectively. Prior methods exploit multi-scale
convolutional networks, which are limited in three aspects: (1) Convolutions
are local operators while identifying the cause of edge formation requires
looking at far away pixels. (2) Priors specific to edge cause are fixed in
prediction heads. (3) Using separate networks for generic and fine-grained edge
detection, and the constraint between them may be violated. To address these
three issues, we propose a two-stage transformer-based network sequentially
predicting generic edges and fine-grained edges, which has a global receptive
field thanks to the attention mechanism. The prior knowledge of edge causes is
formulated as four learnable cause tokens in a cause-aware decoder design.
Furthermore, to encourage the consistency between generic edges and
fine-grained edges, an edge aggregation and alignment loss is exploited. We
evaluate our method on the public benchmark BSDS-RIND and several newly derived
benchmarks, and achieve new state-of-the-art results. Our code, data, and
models are publicly available at https://github.com/Daniellli/ECT.git.