Global Context-Aware Progressive Aggregation Network for Salient Object Detection

• URL: http://arxiv.org/abs/2003.00651v1
• Date: Mon, 2 Mar 2020 04:26:10 GMT
• Title: Global Context-Aware Progressive Aggregation Network for Salient Object Detection
• Authors: Zuyao Chen, Qianqian Xu, Runmin Cong, Qingming Huang
• Abstract summary: 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.
• Score: 117.943116761278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role. Most of the previous works mainly adopted multiple level feature integration yet ignored the gap between different features. Besides, there also exists a dilution process of high-level features as they passed on the top-down pathway. To remedy these issues, we propose a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation (FIA) modules and generate the saliency map in a supervised way. Moreover, a Head Attention (HA) module is used to reduce information redundancy and enhance the top layers features by leveraging the spatial and channel-wise attention, and the Self Refinement (SR) module is utilized to further refine and heighten the input features. Furthermore, we design the Global Context Flow (GCF) module to generate the global context information at different stages, which aims to learn the relationship among different salient regions and alleviate the dilution effect of high-level features. Experimental results on six benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.

Related papers

• FANet: Feature Amplification Network for Semantic Segmentation in Cluttered Background [9.970265640589966]
  Existing deep learning approaches leave out the semantic cues that are crucial in semantic segmentation present in complex scenarios. We propose a feature amplification network (FANet) as a backbone network that incorporates semantic information using a novel feature enhancement module at multi-stages. Our experimental results demonstrate the state-of-the-art performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-07-12T15:57:52Z)
• 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)
• TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment [53.72721476803585]
  Image Quality Assessment (IQA) is a fundamental task in computer vision that has witnessed remarkable progress with deep neural networks. We propose a top-down approach that uses high-level semantics to guide the IQA network to focus on semantically important local distortion regions. A key component of our approach is the proposed cross-scale attention mechanism, which calculates attention maps for lower level features.
  arXiv  Detail & Related papers  (2023-08-06T09:08:37Z)
• Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain [52.783709712318405]
  Unsupervised domain adaptation (UDA) has attracted considerable attention, which transfers knowledge from a label-rich source domain to a related but unlabeled target domain. We propose a novel style-aware feature fusion method (SAFF) to bridge the large domain gap and transfer knowledge while alleviating the loss of class-discnative information.
  arXiv  Detail & Related papers  (2022-09-05T10:06:03Z)
• Perception-and-Regulation Network for Salient Object Detection [8.026227647732792]
  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.
  arXiv  Detail & Related papers  (2021-07-27T02:38:40Z)
• Video Salient Object Detection via Adaptive Local-Global Refinement [7.723369608197167]
  Video salient object detection (VSOD) is an important task in many vision applications. We propose an adaptive local-global refinement framework for VSOD. We show that our weighting methodology can further exploit the feature correlations, thus driving the network to learn more discriminative feature representation.
  arXiv  Detail & Related papers  (2021-04-29T14:14:11Z)
• Global Context Aware RCNN for Object Detection [1.1939762265857436]
  We propose a novel end-to-end trainable framework, called Global Context Aware (GCA) RCNN. The core component of GCA framework is a context aware mechanism, in which both global feature pyramid and attention strategies are used for feature extraction and feature refinement. In the end, we also present a lightweight version of our method, which only slightly increases model complexity and computational burden.
  arXiv  Detail & Related papers  (2020-12-04T14:56:46Z)
• Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers [84.57980167400513]
  Neural Function Modules (NFM) aims to introduce the same structural capability into deep learning. Most of the work in the context of feed-forward networks combining top-down and bottom-up feedback is limited to classification problems. The key contribution of our work is to combine attention, sparsity, top-down and bottom-up feedback, in a flexible algorithm.
  arXiv  Detail & Related papers  (2020-10-15T20:43:17Z)
• 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.