# Uncertainty-Aware Deep Calibrated Salient Object Detection

arXiv: https://arxiv.org/abs/2012.06020 · HTML: https://mlsanity.com/paper/2012.06020/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Transmission-Guided Bayesian Generative Model for Smoke Segmentation](https://mlsanity.com/paper/2303.00900/) (2023)
- [Confidence-Aware Learning for Camouflaged Object Detection](https://mlsanity.com/paper/2106.11641/) (2021)
- [Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model](https://mlsanity.com/paper/2111.11055/) (2021)

## Abstract (arXiv)

Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. However, those methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem. Thus, state-of-the-art SOD networks are prone to be overconfident. In other words, the predicted confidence of the networks does not reflect the real probability of correctness of salient object detection, which significantly hinder their real-world applicability. In this paper, we introduce an uncertaintyaware deep SOD network, and propose two strategies from different perspectives to prevent deep SOD networks from being overconfident. The first strategy, namely Boundary Distribution Smoothing (BDS), generates continuous labels by smoothing the original binary ground-truth with respect to pixel-wise uncertainty. The second strategy, namely Uncertainty-Aware Temperature Scaling (UATS), exploits a relaxed Sigmoid function during both training and testing with spatially-variant temperature scaling to produce softened output. Both strategies can be incorporated into existing deep SOD networks with minimal efforts. Moreover, we propose a new saliency evaluation metric, namely dense calibration measure C, to measure how the model is calibrated on a given dataset. Extensive experimental results on seven benchmark datasets demonstrate that our solutions can not only better calibrate SOD models, but also improve the network accuracy.