Lightweight Contrast Modeling for Attention-Aware Visual Localization

Abstract

Salient object detection, which aims at localizing the attention-aware visual objects, is the indispensable technology for intelligent robots to understand and interact with the complicated environments. Existing salient object detection approaches mainly focus on the optimization of detection performance, while ignoring the considerations for computational resource consumption and algorithm efficiency. Contrarily, we build a superior lightweight network architecture to simultaneously improve performance on both accuracy and efficiency for salient object detection. Specifically, our proposed approach adopts the lightweight bottleneck as its primary building block to significantly reduce the number of parameters and to speed up the process of training and inference. In practice, the visual contrast is insufficiently discovered with the limitation of the small empirical receptive field of CNN. To alleviate this issue, we design a multi-scale convolution module to rapidly discover high-level visual contrast. Moreover, a lightweight refinement module is utilized to restore object saliency details with negligible extra cost. Extensive experiments on efficiency and accuracy trade-offs show that our model is more competitive than the state-of-the-art works on salient object detection task and has prominent potentials for robots applications in real time.