Uncertainty Inspired RGB-D Saliency Detection (2009.03075v1)

Abstract: 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.

• The paper introduces a first stochastic framework for RGB-D saliency detection by leveraging uncertainty in human annotations.
• It integrates a dual inference strategy using a CVAE and Langevin Dynamics-based MCMC to refine latent variable estimation.
• Extensive experiments on six benchmarks show enhanced F- and E-measures, demonstrating improved accuracy and generalization.

Uncertainty Inspired RGB-D Saliency Detection

The paper "Uncertainty Inspired RGB-D Saliency Detection" introduces a novel approach to RGB-D saliency detection that leverages uncertainty in human annotation to improve prediction models. The authors propose the first stochastic framework specifically designed for RGB-D saliency detection, shifting from traditional deterministic methods to a probabilistic model that learns distributions of saliency maps rather than single point estimates.

Research Methodology

The main contribution of this work lies in the design of a generative architecture that incorporates a latent variable to model labeling variations inherent in the data. The framework consists of two core components:

1. Generator Model: An encoder-decoder network tasked with mapping input RGB-D images and a latent variable to produce stochastic saliency predictions.
2. Inference Model: This model progressively refines the latent variable through sampling from either the true or an approximate posterior distribution.

To realize these components, the authors implement two inference strategies. The first is a Conditional Variational Auto-Encoder (CVAE) with an additional encoder to approximate the posterior distribution of the latent variable. The second is the Alternating Back-Propagation (ABP) method, which directly samples the latent variable from the true posterior using Langevin Dynamics-based Markov Chain Monte Carlo (MCMC) methods. This dual approach allows the network to handle uncertainty in labeling more robustly compared to conventional models.

Experimental Validation

The proposed model's performance is substantiated through extensive experiments across six challenging RGB-D benchmark datasets. Quantitative metrics such as mean F-measure and mean E-measure, alongside qualitative assessments, demonstrate the model's enhanced capability in capturing the inherent variations in human annotations, delivering superior forecasts compared to existing models. The paper claims noticeable improvements over recent benchmarks, revealing improved generalization and robustness in handling various saliency contexts.

Implications and Applications

This work's implications span both theoretical and practical domains. Theoretically, integrating a stochastic inference model challenges conventional paradigms around RGB-D saliency detection by effectively modeling probabilistic structures. Practically, such enhanced modeling has applications in numerous computer vision tasks where understanding the saliency is critical, such as robotic vision, autonomous systems, and complex scene understanding.

Future Prospects

Future developments of this research could include extending the stochastic framework to other areas of saliency detection and refining the model to incorporate more diverse types of auxiliary data or advanced learning paradigms, potentially improving real-time performance or accuracy across different environmental conditions. Additionally, exploring multi-annotator datasets could further validate and refine the model's ability to generalize human uncertainty in perception.

This paper contributes significantly to the field by demonstrating the effectiveness of uncertainty modeling in RGB-D saliency detection, setting a foundation for continued exploration of probabilistic approaches in similar computer vision tasks.