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Multi-modal Weights Sharing and Hierarchical Feature Fusion for RGBD Salient Object Detection

Fen Xiao, Bin Li, Yimu Peng, Chunhong Cao, Kai Hu, Xieping Gao
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
First, we propose a CNN-based cross-modal transfer learning, which learn knowledge from sufficient labeled RGB salient object datasets and guide the depth domain feature extraction.  ...  Then we design a feature fusion module to fuse the complementary features in a hierarchical manner. At last, the final saliency map is obtained by integrating multi-scale information step by step.  ...  BCE loss measures both structural and global difference between the predicted saliency map and the ground truth.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2971509">doi:10.1109/access.2020.2971509</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aze3ddzokjcy3pn764iffcibsm">fatcat:aze3ddzokjcy3pn764iffcibsm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108133746/https://ieeexplore.ieee.org/ielx7/6287639/8948470/08981965.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/18/1b/181bad741951dab6c4e489593fe0f160b76057f7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2971509"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

RGB-D salient object detection: A survey

Tao Zhou, Deng-Ping Fan, Ming-Ming Cheng, Jianbing Shen, Ling Shao
<span title="2021-01-07">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jnfwhcgai5dalfpeugj6pkswji" style="color: black;">Computational Visual Media</a> </i> &nbsp;
Finally, we discuss several challenges and open directions of RGB-D based salient object detection for future research.  ...  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.  ...  Acknowledgements This research was supported by a Major Project for a New Generation of AI under Grant No. 2018AAA0100400, National Natural Science Foundation of China (61922046), and Tianjin Natural Science  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s41095-020-0199-z">doi:10.1007/s41095-020-0199-z</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33432275">pmid:33432275</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7788385/">pmcid:PMC7788385</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/foiz2zth4vckjfuhvh524hwdtq">fatcat:foiz2zth4vckjfuhvh524hwdtq</a> </span>
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Employing Bilinear Fusion and Saliency Prior Information for RGB-D Salient Object Detection

Nianchang Huang, Yang Yang, Dingwen Zhang, Qiang Zhang, Jungong Han
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sbzicoknnzc3tjljn7ifvwpooi" style="color: black;">IEEE transactions on multimedia</a> </i> &nbsp;
Index Terms-RGB-D salient object detection, bilinear fusion strategy, saliency prior information guided fusion, saliency refinement and prediction.  ...  for RGB-D saliency detection.  ...  ., color, texture, local and global contrast) to detect the salient objects [15] , [31] , [32] , [14] , [33] , [17] , [16] , [34] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tmm.2021.3069297">doi:10.1109/tmm.2021.3069297</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cvspnkbza5al7nvowh3zf6t7e4">fatcat:cvspnkbza5al7nvowh3zf6t7e4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715170714/https://pure.aber.ac.uk/portal/files/42036032/04_Employing_Bilinear_Fusion_and_Saliency_Prior_Information_for_RGB_D_Salient_Object_Detection.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/bc/5b/bc5bc1100cee476d65f9f40354f91141b2c57d91.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tmm.2021.3069297"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

RGB-D Salient Object Detection: A Survey [article]

Tao Zhou, Deng-Ping Fan, Ming-Ming Cheng, Jianbing Shen, Ling Shao
<span title="2020-11-29">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Finally, we discuss several challenges and open directions of RGB-D based SOD for future research.  ...  In this paper, we provide a comprehensive survey of RGB-D based SOD models from various perspectives, and review related benchmark datasets in detail.  ...  In [51] , a multi-contextual contrast model including local, global, and background contrast was developed to detect salient objects using depth maps.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.00230v3">arXiv:2008.00230v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n52weyun25fq3aug4ughjqs2ru">fatcat:n52weyun25fq3aug4ughjqs2ru</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201203231224/https://arxiv.org/pdf/2008.00230v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/db/2e/db2e1312de3ebe27829bd14111ac808365a29508.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.00230v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection [article]

Xiaoqi Zhao, Lihe Zhang, Youwei Pang, Huchuan Lu, Lei Zhang
<span title="2020-07-15">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Existing RGB-D salient object detection (SOD) approaches concentrate on the cross-modal fusion between the RGB stream and the depth stream.  ...  In this work, we design a single stream network to directly use the depth map to guide early fusion and middle fusion between RGB and depth, which saves the feature encoder of the depth stream and achieves  ...  and Technology Innovation Foundation #2019J12GX039, and the Fundamental Research Funds for the Central Universities # DUT20ZD212.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.06811v2">arXiv:2007.06811v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bl3xni72vrhjzbn47lbrumaycu">fatcat:bl3xni72vrhjzbn47lbrumaycu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200905223956/https://arxiv.org/pdf/2007.06811v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9e/ac/9eac3f2931432ba6d3fa829eafbd3b9198193eaf.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.06811v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

RGBD Salient Object Detection: A Benchmark and Algorithms [chapter]

Houwen Peng, Bing Li, Weihua Xiong, Weiming Hu, Rongrong Ji
<span title="">2014</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
To make sure that most existing RGB saliency models can still be adequate in RGBD scenarios, we continue to provide a simple fusion framework that combines existing RGB-produced saliency with new depth-induced  ...  saliency, the former one is estimated from existing RGB models while the latter one is based on the proposed multi-contextual contrast model.  ...  Due to lacking of global relations and structure, local contrast methods are sensitive to high frequency content or noises.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-10578-9_7">doi:10.1007/978-3-319-10578-9_7</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7a3mdowcefhf5drqxrkaxws4ra">fatcat:7a3mdowcefhf5drqxrkaxws4ra</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170829041548/http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/ECCV_2014/papers/8691/86910092.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ce/33/ce33b12eba6859b8595b922f6a760d4dafb8b9d8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-10578-9_7"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Middle-level Fusion for Lightweight RGB-D Salient Object Detection [article]

Nianchang Huang, Qiang Zhang, Jungong Han
<span title="2021-06-05">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To address these issues, we propose to employ the middle-level fusion structure for designing lightweight RGB-D SOD model in this paper, which first employs two sub-networks to extract low- and middle-level  ...  a lightweight feature-level and decision-level feature fusion (LFDF) module for aggregating the feature-level and the decision-level saliency information in different stages with less parameters.  ...  of different stages with less parameters for better saliency prediction.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.11543v3">arXiv:2104.11543v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hv2ot7rrsjblfbylgu6xzd4p6i">fatcat:hv2ot7rrsjblfbylgu6xzd4p6i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210609231323/https://arxiv.org/pdf/2104.11543v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/48/f1/48f17956f2a4594e274d7f6227dd72f8cd86e777.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.11543v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Improving RGBD Saliency Detection Using Progressive Region Classification and Saliency Fusion

Huan Du, Zhi Liu, Hangke Song, Lin Mei, Zheng Xu
<span title="">2016</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
A random forest regressor is then learned to predict the coarse saliency map and fine saliency map, respectively.  ...  This paper proposes an effective method to improve the saliency detection performance of existing RGBD (RGB image with Depth map) saliency models.  ...  In [26] , the depth features are extracted to guide the saliency ranking of RGB image while the RGB saliency is used as the guide of depth map ranking as well.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2016.2632724">doi:10.1109/access.2016.2632724</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/57dmo4hkp5hobeecpprjl3mite">fatcat:57dmo4hkp5hobeecpprjl3mite</a> </span>
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JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection [article]

Keren Fu and Deng-Ping Fan and Ge-Peng Ji and Qijun Zhao
<span title="2020-04-18">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection.  ...  In contrast, our JL-DCF learns from both RGB and depth inputs through a Siamese network. To this end, we propose two effective components: joint learning (JL), and densely-cooperative fusion (DCF).  ...  Experimental results show the feasibility of learning a shared network for salient object localization in RGB and depth views, simultaneously, to achieve accurate prediction.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.08515v1">arXiv:2004.08515v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gq6kwinm3zdivdlxoyrjwgkbvq">fatcat:gq6kwinm3zdivdlxoyrjwgkbvq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200503004556/https://arxiv.org/pdf/2004.08515v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.08515v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Synergistic saliency and depth prediction for RGB-D saliency detection [article]

Yue Wang, Yuke Li, James H. Elder, Huchuan Lu, Runmin Wu, Lu Zhang
<span title="2020-10-26">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To generalize our method on RGB-D saliency datasets, a novel prediction-guided cross-refinement module which jointly estimates both saliency and depth by mutual refinement between two respective tasks,  ...  This has motivated the development of several RGB-D saliency datasets and algorithms that use all four channels of the RGB-D data for both training and inference.  ...  And E m captures global statistics and local pixel matching information.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.01711v2">arXiv:2007.01711v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7n45wsmfi5hirjkqg6arq5vzdy">fatcat:7n45wsmfi5hirjkqg6arq5vzdy</a> </span>
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Siamese Network for RGB-D Salient Object Detection and Beyond [article]

Keren Fu, Deng-Ping Fan, Ge-Peng Ji, Qijun Zhao, Jianbing Shen, Ce Zhu
<span title="2021-04-16">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Existing RGB-D salient object detection (SOD) models usually treat RGB and depth as independent information and design separate networks for feature extraction from each.  ...  Comprehensive experiments using five popular metrics show that the designed framework yields a robust RGB-D saliency detector with good generalization.  ...  Works [48] and [49] [73] proposed for RGB-D saliency detection a selective self-mutual attention mechanism inspired by the non-local model [74] . Zhang et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.12134v2">arXiv:2008.12134v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4dy5tf2yjngetox4f6x4fsau7q">fatcat:4dy5tf2yjngetox4f6x4fsau7q</a> </span>
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UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders [article]

Jing Zhang, Deng-Ping Fan, Yuchao Dai, Saeed Anwar, Fatemeh Sadat Saleh, Tong Zhang, Nick Barnes
<span title="2020-04-13">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process.  ...  Existing RGB-D saliency detection methods treat the saliency detection task as a point estimation problem, and produce a single saliency map following a deterministic learning pipeline.  ...  [5] proposed a complementary-aware RGB-D saliency detection model by fusing features from the same stage of each modality with a complementary-aware fusion block. Chen et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.05763v1">arXiv:2004.05763v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bk5q4rbn45hgrlyin2pbdwfc2u">fatcat:bk5q4rbn45hgrlyin2pbdwfc2u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200415031239/https://arxiv.org/pdf/2004.05763v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.05763v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Light Field Salient Object Detection: A Review and Benchmark [article]

Keren Fu, Yao Jiang, Ge-Peng Ji, Tao Zhou, Qijun Zhao, Deng-Ping Fan
<span title="2021-07-24">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
, including a comparison between light field SOD and RGB-D SOD models, are achieved.  ...  This paper provides the first comprehensive review and benchmark for light field SOD, which has long been lacking in the saliency community.  ...  E-measure (E φ ) [82] is a recently proposed metric which considers both the local and global similarity between the prediction and ground-truth.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.04968v4">arXiv:2010.04968v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gvwnnhd4m5hlfjaamuupe7ymva">fatcat:gvwnnhd4m5hlfjaamuupe7ymva</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210730093843/https://arxiv.org/pdf/2010.04968v4.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/96/62/966210f3a95c219dfc13613756b636b2203fc968.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.04968v4" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking [article]

Xiao Wang, Xiujun Shu, Shiliang Zhang, Bo Jiang, Yaowei Wang, Yonghong Tian, Feng Wu
<span title="2022-05-09">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The spatial and temporal recurrent neural network is used to capture the direction-aware context for accurate global attention prediction.  ...  To address issues caused by heavy occlusion, fast motion and out-of-view, we propose to conduct a joint local and global search by exploiting a new direction-aware target driven attention mechanism.  ...  Science Foundation of China (61825101, 62027804, 62076004, 62102205, U20B2052), Natural Science Foundation of Anhui Province (2108085Y23), the Postdoctoral Innovative Talent Support Program (BX20200174), and  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.10433v2">arXiv:2107.10433v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3mxe5iidvrgbvbxdna4pwwlv74">fatcat:3mxe5iidvrgbvbxdna4pwwlv74</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220511185548/https://arxiv.org/pdf/2107.10433v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/35/d2/35d238b9a170d7456422f32796ff41cc26f72a57.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.10433v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Salient Object Detection Techniques in Computer Vision—A Survey

Ashish Kumar Gupta, Ayan Seal, Mukesh Prasad, Pritee Khanna
<span title="2020-10-19">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4d3elkqvznfzho6ki7a35bt47u" style="color: black;">Entropy</a> </i> &nbsp;
Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection.  ...  Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision.  ...  [152] integrated pixel-wise local estimate with the object-aware global search for robust saliency detection.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/e22101174">doi:10.3390/e22101174</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33286942">pmid:33286942</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7597345/">pmcid:PMC7597345</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3p5d2nal4vhxbi2via3g7oicga">fatcat:3p5d2nal4vhxbi2via3g7oicga</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201029221456/https://res.mdpi.com/d_attachment/entropy/entropy-22-01174/article_deploy/entropy-22-01174-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fe/c6/fec6834e5d29b064ef4313f288e2112a053a8922.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/e22101174"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597345" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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