Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Multi-class part parsing based on deep learning

Alsudays, Njuod 2024. Multi-class part parsing based on deep learning. PhD Thesis, Cardiff University.
Item availability restricted.

[thumbnail of AlsudaysN, PhD, 2024.pdf] PDF - Accepted Post-Print Version
Restricted to Repository staff only until 22 November 2025 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (58MB) | Request a copy
[thumbnail of Cardiff University Electronic Publication Form] PDF (Cardiff University Electronic Publication Form) - Supplemental Material
Restricted to Repository staff only

Download (638kB) | Request a copy

Abstract

Multi-class part parsing is a dense prediction task that seeks to simultaneously detect multiple objects and the semantic parts within these objects in the scene. This problem is important in providing detailed object understanding but is challenging due to the existence of both class-level and part-level ambiguities. This thesis investigates recent advancements in deep learning to tackle the challenges in multi-class part parsing. First, the AFPSNet network is proposed, which integrates scaled attention and feature fusion to tackle part-level ambiguity and thereby improving parts prediction accuracy. The integration of attention enhances feature representations by focusing on important features, while the feature fusion improves the fusion operation for different scales of features. An object-to-part training strategy is also used to address class-level ambiguity, improving the localisation of parts by exploiting prior knowledge of objects. Building on this foundation, the GRPSNet framework is introduced to further enhance the performance of multi-class part parsing. This framework integrates graph reasoning to capture relationships between parts, thereby improving part segmentation. These captured relationships help to enhance the recognition and localisation of parts. Moreover, the relationships of part boundaries are exploited to further enhance the accuracy of part segmentation. To further refine part segmentation, Multi-Class Boundaries integrated into the AFPSNet network. This integration aims to accurately identify and focus on the spatial boundaries of part classes, thereby enhancing the overall segmentation quality. Experimental results demonstrate the effectiveness of the proposed networks. Various evaluations, including ablation studies and comparisons with existing methods, were conducted on the widely used PASCAL-Part benchmark dataset and the large-scale ADE20K Part benchmark dataset. These experiments validate the research hypotheses, showing notable improvements in part localisation and segmentation accuracy.

Item Type: Thesis (PhD)
Date Type: Completion
Status: Unpublished
Schools: Computer Science & Informatics
Date of First Compliant Deposit: 22 November 2024
Date of Acceptance: 21 November 2024
Last Modified: 25 Nov 2024 09:23
URI: https://orca.cardiff.ac.uk/id/eprint/174221

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics