NeuroIoU: Learning a Surrogate Loss for Semantic Segmentation

Jawahar, C V and Balasubramanian, Vineeth N and Nagendar, G and et al, . (2018) NeuroIoU: Learning a Surrogate Loss for Semantic Segmentation. In: 29th British Machine Vision Conference, BMVC, 3-6 September 2018, Newcastle, United Kingdom.

Full text not available from this repository. (Request a copy)

Abstract

Semantic segmentation is a popular task in computer vision today, and deep neural network models have emerged as the popular solution to this problem in recent times. The typical loss function used to train neural networks for this task is the cross-entropy loss. However, the success of the learned models is measured using Intersection-OverUnion (IoU), which is inherently non-differentiable. This gap between performance measure and loss function results in a fall in performance, which has also been studied by few recent efforts. In this work, we propose a novel method to automatically learn a surrogate loss function that approximates the IoU loss and is better suited for good IoU performance. To the best of our knowledge, this is the first such work that attempts to learn a loss function for this purpose. The proposed loss can be directly applied over any network. We validated our method over different networks (FCN, SegNet, UNet) on the PASCAL VOC and Cityscapes datasets. Our results on this work show consistent improvement over baseline methods.

[error in script]

IITH Creators: