Log In Sign Up

InverseForm: A Loss Function for Structured Boundary-Aware Segmentation

by   Shubhankar Borse, et al.

We present a novel boundary-aware loss term for semantic segmentation using an inverse-transformation network, which efficiently learns the degree of parametric transformations between estimated and target boundaries. This plug-in loss term complements the cross-entropy loss in capturing boundary transformations and allows consistent and significant performance improvement on segmentation backbone models without increasing their size and computational complexity. We analyze the quantitative and qualitative effects of our loss function on three indoor and outdoor segmentation benchmarks, including Cityscapes, NYU-Depth-v2, and PASCAL, integrating it into the training phase of several backbone networks in both single-task and multi-task settings. Our extensive experiments show that the proposed method consistently outperforms baselines, and even sets the new state-of-the-art on two datasets.


page 1

page 4

page 5

page 7

page 8


Joint Semantic Segmentation and Boundary Detection using Iterative Pyramid Contexts

In this paper, we present a joint multi-task learning framework for sema...

Active Boundary Loss for Semantic Segmentation

This paper proposes a novel active boundary loss for semantic segmentati...

SEMEDA: Enhancing Segmentation Precision with Semantic Edge Aware Loss

While nowadays deep neural networks achieve impressive performances on s...

Boundary-Aware Network for Fast and High-Accuracy Portrait Segmentation

Compared with other semantic segmentation tasks, portrait segmentation r...

Boundary Loss for Remote Sensing Imagery Semantic Segmentation

In response to the growing importance of geospatial data, its analysis i...

Distance Map Loss Penalty Term for Semantic Segmentation

Convolutional neural networks for semantic segmentation suffer from low ...

Learning Semantic Segmentation from Multiple Datasets with Label Shifts

With increasing applications of semantic segmentation, numerous datasets...

Code Repositories