Perception-and-Regulation Network for Salient Object Detection

• URL: http://arxiv.org/abs/2107.12560v1
• Date: Tue, 27 Jul 2021 02:38:40 GMT
• Title: Perception-and-Regulation Network for Salient Object Detection
• Authors: Jinchao Zhu, Xiaoyu Zhang, Xian Fang, Junnan Liu
• Abstract summary: We propose a novel global attention unit that adaptively regulates the feature fusion process by explicitly modeling interdependencies between features. The perception part uses the structure of fully-connected layers in classification networks to learn the size and shape of objects. An imitating eye observation module (IEO) is further employed for improving the global perception ability of the network.
• Score: 8.026227647732792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effective fusion of different types of features is the key to salient object detection. The majority of existing network structure design is based on the subjective experience of scholars and the process of feature fusion does not consider the relationship between the fused features and highest-level features. In this paper, we focus on the feature relationship and propose a novel global attention unit, which we term the "perception- and-regulation" (PR) block, that adaptively regulates the feature fusion process by explicitly modeling interdependencies between features. The perception part uses the structure of fully-connected layers in classification networks to learn the size and shape of objects. The regulation part selectively strengthens and weakens the features to be fused. An imitating eye observation module (IEO) is further employed for improving the global perception ability of the network. The imitation of foveal vision and peripheral vision enables IEO to scrutinize highly detailed objects and to organize the broad spatial scene to better segment objects. Sufficient experiments conducted on SOD datasets demonstrate that the proposed method performs favorably against 22 state-of-the-art methods.

Related papers

• Localization, balance and affinity: a stronger multifaceted collaborative salient object detector in remote sensing images [24.06927394483275]
  We propose a stronger multifaceted collaborative salient object detector in ORSIs, termed LBA-MCNet. The network focuses on accurately locating targets, balancing detailed features, and modeling image-level global context information.
  arXiv  Detail & Related papers  (2024-10-31T14:50:48Z)
• Global Feature Pyramid Network [1.2473780585666772]
  The visual feature pyramid has proven its effectiveness and efficiency in target detection tasks. Current methodologies tend to overly emphasize inter-layer feature interaction, neglecting the crucial aspect of intra-layer feature adjustment.
  arXiv  Detail & Related papers  (2023-12-18T14:30:41Z)
• Salient Object Detection in Optical Remote Sensing Images Driven by Transformer [69.22039680783124]
  We propose a novel Global Extraction Local Exploration Network (GeleNet) for Optical Remote Sensing Images (ORSI-SOD) Specifically, GeleNet first adopts a transformer backbone to generate four-level feature embeddings with global long-range dependencies. Extensive experiments on three public datasets demonstrate that the proposed GeleNet outperforms relevant state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-15T07:14:43Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• Feature Aggregation and Propagation Network for Camouflaged Object Detection [42.33180748293329]
  Camouflaged object detection (COD) aims to detect/segment camouflaged objects embedded in the environment. Several COD methods have been developed, but they still suffer from unsatisfactory performance due to intrinsic similarities between foreground objects and background surroundings. We propose a novel Feature Aggregation and propagation Network (FAP-Net) for camouflaged object detection.
  arXiv  Detail & Related papers  (2022-12-02T05:54:28Z)
• Centralized Feature Pyramid for Object Detection [53.501796194901964]
  Visual feature pyramid has shown its superiority in both effectiveness and efficiency in a wide range of applications. In this paper, we propose a OLO Feature Pyramid for object detection, which is based on a globally explicit centralized feature regulation.
  arXiv  Detail & Related papers  (2022-10-05T08:32:54Z)
• Salient Object Detection via Integrity Learning [104.13483971954233]
  Integrity is the concept of highlighting all parts that belong to a certain salient object. To facilitate integrity learning for salient object detection, we design a novel Integrity Cognition Network (ICON) ICON explores three important components to learn strong integrity features.
  arXiv  Detail & Related papers  (2021-01-19T14:53:12Z)
• Towards Accurate Camouflaged Object Detection with Mixture Convolution and Interactive Fusion [45.45231015502287]
  We propose a novel deep learning based COD approach, which integrates the large receptive field and effective feature fusion into a unified framework. Our method detects camouflaged objects with an effective fusion strategy, which aggregates the rich context information from a large receptive field.
  arXiv  Detail & Related papers  (2021-01-14T16:06:08Z)
• Global Context-Aware Progressive Aggregation Network for Salient Object Detection [117.943116761278]
  We propose a novel network named GCPANet to integrate low-level appearance features, high-level semantic features, and global context features. We show that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2020-03-02T04:26:10Z)
• Cross-layer Feature Pyramid Network for Salient Object Detection [102.20031050972429]
  We propose a novel Cross-layer Feature Pyramid Network to improve the progressive fusion in salient object detection. The distributed features per layer own both semantics and salient details from all other layers simultaneously, and suffer reduced loss of important information.
  arXiv  Detail & Related papers  (2020-02-25T14:06:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.