Uncertainty Inspired RGB-D Saliency Detection

by   Jing Zhang, et al.

We propose the first stochastic framework to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection models treat this task as a point estimation problem by predicting a single saliency map following a deterministic learning pipeline. We argue that, however, the deterministic solution is relatively ill-posed. Inspired by the saliency data labeling process, we propose a generative architecture to achieve probabilistic RGB-D saliency detection which utilizes a latent variable to model the labeling variations. Our framework includes two main models: 1) a generator model, which maps the input image and latent variable to stochastic saliency prediction, and 2) an inference model, which gradually updates the latent variable by sampling it from the true or approximate posterior distribution. The generator model is an encoder-decoder saliency network. To infer the latent variable, we introduce two different solutions: i) a Conditional Variational Auto-encoder with an extra encoder to approximate the posterior distribution of the latent variable; and ii) an Alternating Back-Propagation technique, which directly samples the latent variable from the true posterior distribution. Qualitative and quantitative results on six challenging RGB-D benchmark datasets show our approach's superior performance in learning the distribution of saliency maps. The source code is publicly available via our project page: https://github.com/JingZhang617/UCNet.


page 2

page 9

page 11


Energy-Based Generative Cooperative Saliency Prediction

Conventional saliency prediction models typically learn a deterministic ...

Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection

In this paper, we propose a noise-aware encoder-decoder framework to dis...

xAI-CycleGAN, a Cycle-Consistent Generative Assistive Network

In the domain of unsupervised image-to-image transformation using genera...

Importance Weighted Adversarial Variational Autoencoders for Spike Inference from Calcium Imaging Data

The Importance Weighted Auto Encoder (IWAE) objective has been shown to ...

Differential equation and probability inspired graph neural networks for latent variable learning

Probabilistic theory and differential equation are powerful tools for th...

Bayesian inference for network Poisson models

This work is motivated by the analysis of ecological interaction network...

Hawkes Processes with Delayed Granger Causality

We aim to explicitly model the delayed Granger causal effects based on m...

Please sign up or login with your details

Forgot password? Click here to reset