PAANet:Visual Perception based Four-stage Framework for Salient Object Detection using High-order Contrast Operator

TL;DR

PAANet introduces a four-stage saliency detection framework inspired by human vision, utilizing a novel high-order contrast operator for improved semantic feature extraction and efficiency, achieving state-of-the-art results.

Contribution

The paper proposes a four-stage framework combining pre-attentive and attentive processes, with a novel contrast operator for enhanced saliency detection in complex scenes.

Findings

Outperforms state-of-the-art methods on five datasets

Efficient training due to fixed backbone in GFE stage

High-order contrast operator improves semantic feature extraction

Abstract

It is believed that human vision system (HVS) consists of pre-attentive process and attention process when performing salient object detection (SOD). Based on this fact, we propose a four-stage framework for SOD, in which the first two stages match the \textbf{P}re-\textbf{A}ttentive process consisting of general feature extraction (GFE) and feature preprocessing (FP), and the last two stages are corresponding to \textbf{A}ttention process containing saliency feature extraction (SFE) and the feature aggregation (FA), namely \textbf{PAANet}. According to the pre-attentive process, the GFE stage applies the fully-trained backbone and needs no further finetuning for different datasets. This modification can greatly increase the training speed. The FP stage plays the role of finetuning but works more efficiently because of its simpler structure and fewer parameters. Moreover, in SFE stage we design for saliency feature extraction a novel contrast operator, which works more semantically in contrast with the traditional convolution operator when extracting the interactive information between the foreground and its surroundings. Interestingly, this contrast operator can be cascaded to form a deeper structure and extract higher-order saliency more effective for complex scene. Comparative experiments with the state-of-the-art methods on 5 datasets demonstrate the effectiveness of our framework.