Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation
This addresses the need for robust segmentation methods in computer vision, particularly for tasks like salient object detection and general segmentation, by reducing reliance on accurate class labels, though it is incremental as it builds on existing architectures like ResNet101 and DeepLab-v3.
The paper tackles the problem of segmentation without predefined class labels by introducing a class-agnostic segmentation (CAS) loss that clusters regions with similar appearance in a weakly-supervised manner, resulting in performance gains such as outperforming state-of-the-art methods by around 50% on salient object detection datasets with low-fidelity training data.
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 first 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 then also test on general segmentation dataset, where class-agnostic segmentation loss outperforms competing losses by huge margins.