Learning to rank salient objects using transformers and graph reasoningTools Bowen, Deng (2025) Learning to rank salient objects using transformers and graph reasoning. PhD thesis, University of Nottingham.
AbstractThis thesis explores the domain of salient object detection, aiming to find the most visually important objects within a given image. Many of the current approaches have focused on datasets with many images containing only a single salient object located towards the center. We focus here on the more complex task of images containing multiple objects, where relative saliency between objects must also be evaluated. A novel multiple salient object detection framework is proposed, utilizing both spatial and channel-wise non-local blocks within a convolutional network. The experiments compare the approach against 14 state-of-the-art methods on five widely used SOD benchmarks and a newly curated multi-object dataset. The proposed method exceeds all previous state-of-the-art approaches in three evaluation metrics and provides a further performance boost against competing techniques on the proposed dataset.
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