ICNet: Information Conversion Network for RGB-D Based Salient Object Detection

Li, Gongyang; Li, Zhi; Ling, Haibin

doi:10.1109/tip.2020.2976689

Cited by 206 publications

(115 citation statements)

References 43 publications

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“…Nevertheless, the boundary details are still not accurate enough especially in high resolution images [53], which will be further improved in future works. We also found that some new approaches with high performance are published after this submission, such as ICNet [26], UCNet [55], JL-DCF [18], A2dele [44], and SSF [59]. We will make a more comprehensive comparison in our extended work.…”

Section: Discussionmentioning

confidence: 95%

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

Chen

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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: Discussionmentioning

confidence: 95%

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

Chen

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

315

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“…To effectively explore the correlations between RGB images and depth maps, several methods propose a multi-scale fusion strategy [42,43,55,109,116,122,123,128]. These models can be divided into two categories.…”

Section: Multi-scale Fusionmentioning

confidence: 99%

“…ICNet [42] proposes an information conversion module to interactively convert high-level features. In this model, a cross-modal depth-weighted combination (CDC) block is introduced to enhance RGB features with depth features at different levels.…”

Section: Multi-scale Fusionmentioning

confidence: 99%

“…Despite the effectiveness of traditional methods using handcrafted features, their low-level features tend Fig. 1 RGB-D based salient object prediction on a sample image using two classical models: DCMC [36] and SE [37], and seven stateof-the-art deep models: D 3 Net [38], SSF [39], A2dele [40], S 2 MA [41], ICNet [42], JL-DCF [43], and UC-Net [44].…”

Section: Introductionmentioning

confidence: 99%

“…DF [52] was the first model to introduce deep learning technology into the RGB-D based salient object detection task. More recently, various deep learning-based models [41][42][43][44][53][54][55] have focused on exploiting effective multi-modal correlations and multi-scale or level information to boost salient object detection performance. To more clearly describe the progress in the RGB-D based salient object detection field, we provide a brief chronology in Fig.…”

Section: Introductionmentioning

confidence: 99%

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RGB-D salient object detection: A survey

et al. 2021

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Salient object detection, which simulates human visual perception in locating the most significant object(s) in a scene, has been widely applied to various computer vision tasks. Now, the advent of depth sensors means that depth maps can easily be captured; this additional spatial information can boost the performance of salient object detection. Although various RGB-D based salient object detection models with promising performance have been proposed over the past several years, an in-depth understanding of these models and the challenges in this field remains lacking. In this paper, we provide a comprehensive survey of RGB-D based salient object detection models from various perspectives, and review related benchmark datasets in detail. Further, as light fields can also provide depth maps, we review salient object detection models and popular benchmark datasets from this domain too. Moreover, to investigate the ability of existing models to detect salient objects, we have carried out a comprehensive attribute-based evaluation of several representative RGB-D based salient object detection models. Finally, we discuss several challenges and open directions of RGB-D based salient object detection for future research. All collected models, benchmark datasets, datasets constructed for attribute-based evaluation, and related code are publicly available at https://github.com/taozh2017/RGBD-SODsurvey.

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Dynamic convolutional neural network based e‐waste management and optimized collection planning

Latha

Kalaiselvi

Sankriti

et al. 2022

Concurrency and Computation

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Electronic waste, also known as e‐waste, refers to electrical or electronic devices that are discarded from households and workplaces. These used e‐wastes are meant to be renovated, reused, recycled, or disposed of, and the processing of these wastes often causes disease and harms the environment. As a result, it is important to handle waste and collect it from the disposal site on a regular basis. Besides, in order to separate precious metals from discarded waste, it is important to identify them by category. Therefore, this article proposes a novel method known as e‐waste management by exploiting the dynamic convolutional neural network (DCNN). This enhances the classification accuracy with the aid of exactly mapping the features of the images. Meanwhile, the collection of waste can be optimized in order to reduce the distance and time. The e‐wastes in the smart garbage bin are frequently monitored by smartphone applications to collect the waste on time. Moreover, it also significantly reduces the training error, classification error, localization error, and validation error on the test images. The experimental depicts that the proposed method hones up the classification accuracy to the great extent.

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ICNet: Information Conversion Network for RGB-D Based Salient Object Detection

Cited by 206 publications

References 43 publications

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

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

RGB-D salient object detection: A survey

Dynamic convolutional neural network based e‐waste management and optimized collection planning

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