Effective deep leaning methodologies for salient object detection

Abstract

Salient object detection has achieved great improvement by using deep neural networks. Diverse challenging problems arise from this computer vision task and attract more attention from researchers. This thesis investigates the issues in specific tasks and aims at proposing effective methodologies to tackle the problems and improve detection performance. More specifically, existing network architectures may cause dilution problems in highlevel semantic information during up-sample operations in the top-down pathway of the Feature Pyramid Network (FPN). In order to overcome this limitation, we propose a novel pyramid self-attention module (PSAM) and the adoption of an independent feature-complementing strategy. In addition, a channel-wise attention module is also employed to reduce redundant features of the feature pyramid network and provide refined results. Introducing depth information in a suboptimal fusion strategy may have a negative influence on the performance of SOD. We discuss the advantages of the so-called progressive multi-scale fusion method and propose a mask-guided feature aggregation module (MGFA). We also introduce a mask-guided refinement module (MGRM) to complement the high-level semantic features and reduce the irrelevant features from multi-scale fusion, leading to an overall refinement of detection. RGB-D methods sacrifice the model size to improve the detection accuracy, which may impede the practical application of SOD problems. To tackle this dilemma, we propose a dynamic distillation method along with simple noise elimination. To this end, the final model can significantly reduce the computational burden while maintaining the validity and mitigating the impact of distorted training data caused by low-quality depth maps. Furthermore, in cosaliency detection, we propose a novel adaptive intra-group aggregation (AIGA) method to model the relationship between individual feature representation in a single image and group feature representation. This proposed AIGA can effectively improve the performance without increasing extra network parameters.