# Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation

arXiv: https://arxiv.org/abs/2010.14793 · HTML: https://mlsanity.com/paper/2010.14793/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient Images](https://mlsanity.com/paper/2104.01526/) (2021)
- [CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation](https://mlsanity.com/paper/2112.04966/) (2021)
- [Shape-Tailored Deep Neural Networks](https://mlsanity.com/paper/2102.08497/) (2021)
- [Region-Transformer: Self-Attention Region Based Class-Agnostic Point Cloud Segmentation](https://mlsanity.com/paper/2403.01407/) (2024)

## Abstract (arXiv)

In this paper we present a novel loss function, called class-agnostic segmentation (CAS) loss. With CAS loss the class descriptors are learned during training of the network. We don't require to define the label of a class a-priori, rather the CAS loss clusters regions with similar appearance together in a weakly-supervised manner. Furthermore, we show that the CAS loss function is sparse, bounded, and robust to class-imbalance. We apply our CAS loss function with fully-convolutional ResNet101 and DeepLab-v3 architectures to the binary segmentation problem of salient object detection. We investigate the performance against the state-of-the-art methods in two settings of low and high-fidelity training data on seven salient object detection datasets. For low-fidelity training data (incorrect class label) class-agnostic segmentation loss outperforms the state-of-the-art methods on salient object detection datasets by staggering margins of around 50%. For high-fidelity training data (correct class labels) class-agnostic segmentation models perform as good as the state-of-the-art approaches while beating the state-of-the-art methods on most datasets. In order to show the utility of the loss function across different domains we also test on general segmentation dataset, where class-agnostic segmentation loss outperforms cross-entropy based loss by huge margins on both region and edge metrics.