Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning approach to adaptively weight multimodal data for improved saliency detection, addressing data quality issues often overlooked in existing methods.

Contribution

It proposes a novel quality-aware neural network that uses DRL to dynamically assign weights to different data modalities based on their contribution to saliency detection.

Findings

Improved saliency detection accuracy on benchmark datasets.

Effective adaptive weighting of multimodal data improves results.

Demonstrated robustness across different types of multimodal inputs.

Abstract

Incorporating various modes of information into the machine learning procedure is becoming a new trend. And data from various source can provide more information than single one no matter they are heterogeneous or homogeneous. Existing deep learning based algorithms usually directly concatenate features from each domain to represent the input data. Seldom of them take the quality of data into consideration which is a key issue in related multimodal problems. In this paper, we propose an efficient quality-aware deep neural network to model the weight of data from each domain using deep reinforcement learning (DRL). Specifically, we take the weighting of each domain as a decision-making problem and teach an agent learn to interact with the environment. The agent can tune the weight of each domain through discrete action selection and obtain a positive reward if the saliency results are improved. The target of the agent is to achieve maximum rewards after finished its sequential action selection. We validate the proposed algorithms on multimodal saliency detection in a coarse-to-fine way. The coarse saliency maps are generated from an encoder-decoder framework which is trained with content loss and adversarial loss. The final results can be obtained via adaptive weighting of maps from each domain. Experiments conducted on two kinds of salient object detection benchmarks validated the effectiveness of our proposed quality-aware deep neural network.