Practical Edge Detection via Robust Collaborative Learning
Yuanbin Fu Xiaojie Guo
Edge detection, as a core component in a wide range of visionoriented tasks,
is to identify object boundaries and prominent edges in natural images. An edge
detector is desired to be both efficient and accurate for practical use. To
achieve the goal, two key issues should be concerned: 1) How to liberate deep
edge models from inefficient pre-trained backbones that are leveraged by most
existing deep learning methods, for saving the computational cost and cutting
the model size; and 2) How to mitigate the negative influence from noisy or
even wrong labels in training data, which widely exist in edge detection due to
the subjectivity and ambiguity of annotators, for the robustness and accuracy.
In this paper, we attempt to simultaneously address the above problems via
developing a collaborative learning based model, termed PEdger. The principle
behind our PEdger is that, the information learned from different training
moments and heterogeneous (recurrent and non recurrent in this work)
architectures, can be assembled to explore robust knowledge against noisy
annotations, even without the help of pre-training on extra data. Extensive
ablation studies together with quantitative and qualitative experimental
comparisons on the BSDS500 and NYUD datasets are conducted to verify the
effectiveness of our design, and demonstrate its superiority over other
competitors in terms of accuracy, speed, and model size. Codes can be found at
https://github.co/ForawardStar/PEdger.