DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection

Abstract

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This paper, published in 1950, received 601 indexed citations. Written by Nian Liu and Junwei Han covering the research area of Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (579 citations), Cognitive Neuroscience (153 citations) and Sensory Systems (99 citations). Published in .

Countries where authors are citing DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection

Since Specialization
Citations

This map shows the geographic impact of DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection more than expected).

Fields of papers citing DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1109/cvpr.2016.80.

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