Effective deep leaning methodologies for salient object detection
File(s)
Author(s)
Ren, Guangyu
Type
Thesis
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.
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.
Version
Open Access
Date Issued
2023-01
Date Awarded
2023-03
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Stathaki, Tania
Publisher Department
Electrical and Electronic Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)