Curiosity-Driven Salient Object Detection With Fragment Attention
Recent deep learning based salient object detection methods with attention mechanisms have made great success. However, existing attention mechanisms can be generally separated into two categories. One part chooses to calculate weights indiscriminately, which yields computational redundancy. While o...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 31(2022) vom: 26., Seite 5989-6001 |
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| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | Recent deep learning based salient object detection methods with attention mechanisms have made great success. However, existing attention mechanisms can be generally separated into two categories. One part chooses to calculate weights indiscriminately, which yields computational redundancy. While one part focuses randomly on a small part of the images, such as hard attention, resulting in incorrectness owing to insufficiently targeted selection of a subset of tokens. To alleviate these problems, we design a Curiosity-driven Network (CNet) and a Curiosity-driven Learning Algorithm (CLA) based on fragment attention (FA) mechanism newly defined in this paper. FA imitates the process of cognition perception driven by human curiosity, and divides the degree of curiosity into three levels, i.e. curious, a little curious and not curious. These three levels correspond to five saliency degrees, including salient and non-salient, likewise salient and likewise non-salient, completely uncertain. With more knowledge gained by the network, CLA transforms the curiosity degree of each pixel to yield enhanced detail-enriched saliency maps. In order to extract more context-aware information of potential salient objects and make a better foundation for CLA, a high-level feature extraction module (HFEM) is further proposed. Based on the much better high-level features extracted by HFEM, FA can classify the curiosity degree for each pixel more reasonably and accurately. Extensive experiments on five popular datasets clearly demonstrate that our method outperforms the state-of-the-art approaches without any pre-processing operations or post-processing operations |
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| Beschreibung: | Date Completed 22.09.2022 Date Revised 22.09.2022 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2022.3203605 |