Embedding Attention and Residual Network for Accurate Salient Object Detection

Chen, Shuhan; Wang, Ben; Tan, Xiuli; Hu, Xuelong

doi:10.1109/tcyb.2018.2879859

Cited by 60 publications

(11 citation statements)

References 59 publications

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“…Top-down Guidance. The deep layer contains high-level semantic information, which can be used as a guidance to help shallow layers filter out noisy distraction [6]. Such a top-down guidance manner was also widely applied in existing methods.…”

Section: Rgb Salient Object Detectionmentioning

confidence: 99%

Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection

Chen

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

316

315

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In this paper, we aim to develop an efficient and compact deep network for RGB-D salient object detection, where the depth image provides complementary information to boost performance in complex scenarios. Starting from a coarse initial prediction by a multi-scale residual block, we propose a progressively guided alternate refinement network to refine it. Instead of using ImageNet pre-trained backbone network, we first construct a lightweight depth stream by learning from scratch, which can extract complementary features more efficiently with less redundancy. Then, different from the existing fusion based methods, RGB and depth features are fed into proposed guided residual (GR) blocks alternately to reduce their mutual degradation. By assigning progressive guidance in the stacked GR blocks within each side-output, the false detection and missing parts can be well remedied. Extensive experiments on seven benchmark datasets demonstrate that our model outperforms existing state-of-the-art approaches by a large margin, and also shows superiority in efficiency (71 FPS) and model size (64.9 MB).

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Section: Rgb Salient Object Detectionmentioning

confidence: 99%

Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection

Chen

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

316

315

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“…The proposed method (LGFAN) is compared with eight state-of-the-art methods, including ASNet [40], BASNet [20], CPD [27] , DSS [16], EAR [41], GateNet [42], PoolNet [19] and R 3 Net [43]. To ensure the authenticity of the conclusions, the sources of prediction results adopted for model performance evaluation were all saliency maps published or generated from the original code.…”

Section: Resultsmentioning

confidence: 99%

Local and Global Feature Aggregation-Aware Network for Salient Object Detection

Gao

Xue

et al. 2022

Electronics

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With the rise of deep learning technology, salient object detection algorithms based on convolutional neural networks (CNNs) are gradually replacing traditional methods. The majority of existing studies, however, focused on the integration of multi-scale features, thereby ignoring the characteristics of other significant features. To address this problem, we fully utilized the features to alleviate redundancy. In this paper, a novel CNN named local and global feature aggregation-aware network (LGFAN) has been proposed. It is a combination of the visual geometry group backbone for feature extraction, an attention module for high-quality feature filtering, and an aggregation module with a mechanism for rich salient features to ease the dilution process on the top-down pathway. Experimental results on five public datasets demonstrated that the proposed method improves computational efficiency while maintaining favorable performance.

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“…An attention [41], [42] is intuited from visual attentions of human beings (incline to be attracted by more important parts of a target object). Attention is widely used in many fields, including object detection [43], [44], prediction [45], query suggestion [46], and recommendation [4]. In brief, attention can be used to increase the interpretability and adaptivity of complex models such as neural networks by calculating the weights of different data/information automatically.…”

Section: Attention Mechanismmentioning

confidence: 99%

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

Wang

Zhang

Xiang

et al. 2022

IEEE Trans. Cybern.

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Recommender systems are important approaches for dealing with the information overload problem in big data era, and various kinds of auxiliary information, including time and sequential information, can help to improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model named Multivariate Hawkes Process Embedding with attention (MHPE-a), which combines a temporal point process with attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform accurate sequential recommendation to satisfy target users' realtime requirement based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines.

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Embedding Attention and Residual Network for Accurate Salient Object Detection

Cited by 60 publications

References 59 publications

Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection

Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection

Local and Global Feature Aggregation-Aware Network for Salient Object Detection

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

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