HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Pushing the boundaries of boundary detection using deep learning

Abstract : In this work we show that adapting Deep Convolutional Neural Network training to the task of boundary detection can result in substantial improvements over the current state-of-the-art in boundary detection. Our contributions consist firstly in combining a careful design of the loss for boundary detection training, a multi-resolution architecture and training with external data to improve the detection accuracy of the current state of the art. When measured on the standard Berkeley Segmentation Dataset, we improve theopti-mal dataset scale F-measure from 0.780 to 0.808 - while human performance is at 0.803. We further improve performance to 0.813 by combining deep learning with grouping, integrating the Normalized Cuts technique within a deep network. We also examine the potential of our boundary detector in conjunction with the task of semantic segmentation and demonstrate clear improvements over state-of-the-art systems. Our detector is fully integrated in the popular Caffe framework and processes a 320x420 image in less than a second.
Document type :
Conference papers
Complete list of metadata

Contributor : Kamilia Abdani Connect in order to contact the contributor
Submitted on : Wednesday, January 8, 2020 - 3:47:05 PM
Last modification on : Thursday, February 3, 2022 - 3:01:40 AM


  • HAL Id : hal-02432711, version 1


Iasonas Kokkinos. Pushing the boundaries of boundary detection using deep learning. 4th International Conference on Learning Representations, ICLR 2016, May 2016, San Juan, Puerto Rico. ⟨hal-02432711⟩



Record views