SBIR-BYOL: a self-supervised sketch-based image retrieval model

Saavedra, José M.; Morales, Javier; Murrugarra-Llerena, Nils

doi:10.1007/s00521-022-07978-9

Cited by 3 publications

(6 citation statements)

References 35 publications

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“…Coarsegrained SBIR CNN [9], [12], [17], [18], [27], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50],…”

Section: Sbir Model Papersmentioning

confidence: 99%

“…[10], [12], [18], [26], [27], [28], [48], [51], [66], [67], [68], [69], [70], [71], [72], [73] RNN+CNN [30], [31], [32], [74], [75] [41] used sketches and natural images to co-train CNNs, prior to which a specific image scaling method and a multi-angle voting scheme were designed for image data to be used together for SBIR. Bui, et al [18] proposed a triplet ranked CNN for SBIR to learn embeddings between sketches and images with significantly improved performance.…”

Section: Ann [65] Cnnmentioning

confidence: 99%

“…Although scientific research methods and reasonable retrieval strategies are used whenever possible, it is still possible to miss some valuable research. [18], [40], [42], [44], [47], [48], [49], [59], [62], [63], [64], [67], [72], [93], [102], [ [62], [63], [64] Sketchy [26] 75471sketches, 12500 photos 125 Public [26], [29], [38], [43], [44], [45], [47], [48], [49], [50], [51], [52], [55], [67], [71], [73], [74], [80], [86], [87], [92], [94], [99], [101], [104], [107], [108], [109], [110],…”

Section: ) Approaches Used In Zero-shot Sbirmentioning

confidence: 99%

“…[9], [12], [17], [18], [28], [29], [30], [31], [32], [38], [40], [41], [42], [44], [47], [48], [49], [50], [51], [55], [56], [60], [63], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [79], [81], [82], [83], [84], [87], [88], [89], [90], [93], [94], [95], [96], [97], [99], [100], [101], <...…”

Section: Rq3: What Are the Various Metrics For Evaluating The Perform...unclassified

“…[82] mAP [17], [18], [32], [38], [40], [41], [42], [44], [45], [46], [47], [48], [49], [51], [56], [59], [60], [61], [64], [66], [67], [69], [72], [84], [91], [93], [96], [97], [98], [99], [100], [101], [102], [103], [104], [105], [107], [108], [109], [110], [111], [112], [113], [114], [115] memory loads [38], [58], [61]…”

Section: Ap (Average Precision)mentioning

confidence: 99%

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A Systematic Literature Review of Deep Learning Approaches for Sketch-Based Image Retrieval: Datasets, Metrics, and Future Directions

Yang,

Ismail,

Pang

et al. 2024

IEEE Access

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Sketch-based image retrieval (SBIR) utilizes sketches to search for images containing similar objects or scenes. Due to the proliferation of touch-screen devices, sketching has become more accessible and therefore has received increasing attention. Deep learning has emerged as a potential tool for SBIR, allowing models to automatically extract image features and learn from large amounts of data. To the best of our knowledge, there is currently no systematic literature review (SLR) of SBIR with deep learning. Therefore, the aim of this review is to incorporate related works into a systematic study, highlighting the main contributions of individual researchers over the years, with a focus on past, present and future trends. To achieve the purpose of this study, 90 studies from 2016 to June 2023 in 4 databases were collected and analyzed using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework. The specific models, datasets, evaluation metrics, and applications of deep learning in SBIR are discussed in detail. This study found that Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) are the most widely used deep learning methods for SBIR. A commonly used dataset is Sketchy, especially in the latest Zero-shot sketch-based image retrieval (ZS-SBIR) task. The results show that Mean Average Precision (mAP) is the most commonly used metric for quantitative evaluation of SBIR. Finally, we provide some future directions and guidance for researchers based on the results of this review.INDEX TERMS Sketch-based image retrieval, SBIR, SLR, PRISMA, deep learning.

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“…Coarsegrained SBIR CNN [9], [12], [17], [18], [27], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50],…”

Section: Sbir Model Papersmentioning

confidence: 99%

Section: Ann [65] Cnnmentioning

confidence: 99%

Section: ) Approaches Used In Zero-shot Sbirmentioning

confidence: 99%

Section: Rq3: What Are the Various Metrics For Evaluating The Perform...unclassified

Section: Ap (Average Precision)mentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Literature Review of Deep Learning Approaches for Sketch-Based Image Retrieval: Datasets, Metrics, and Future Directions

Yang,

Ismail,

Pang

et al. 2024

IEEE Access

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Unsupervised contrastive learning: Shale porosity prediction based on conventional well logging

Qiao,

Yang,

et al. 2024

Physics of Fluids

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Porosity is a pivotal factor affecting the capacity for storage and extraction in shale reservoirs. The paucity of labeled data in conventional well logs interpretation and supervised learning models leads to inadequate generalization and diminished prediction accuracy, thus limiting their effectiveness in precise porosity evaluation. This study introduces a contrastive learning – convolutional neural network (CL-CNN) framework that utilizes CL for pretraining on a vast array of unlabeled data, followed by fine-tuning using a traditional CNN on a curated set of labeled data. Applied to the Subei Basin in Eastern China, the framework was tested on 130 labeled data and 2576 unlabeled data points from well H1. The results indicate that the CL-CNN framework outperforms traditional CNN-based supervised learning and other machine learning models in terms of prediction accuracy for the dataset under consideration. Furthermore, it demonstrates the potential for extensive porosity assessment across different logged depths. Due to its efficacy and ease of use, the proposed framework is versatile enough for application in reservoir evaluation, engineering development, and related fields. The innovative contribution of this research is encapsulated in its unique methodology and procedural steps for the accurate prediction of shale reservoir porosity, thus significantly enriching the existing body of knowledge in this domain.

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Cross Domain Visual Search with Feature Learning using Multi-stream Transformer-based Architectures

Ribeiro¹

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Because the work is never separate from who we are, I would also like to thank my family; my parents and siblings, for their undying support and love, and my girlfriend Lais Miranda, who is my safe haven and my partner-for-life and who wished this section mentioned her as my fiancé but in her words, her "idea for a proposal is too grand and it must be done this way, so we'll be girlfriends a little longer'. I extend these thanks also to my friends, who lovingly listened to me going on about random specific interests and complaining about academia and the patriarchy:

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SBIR-BYOL: a self-supervised sketch-based image retrieval model

Cited by 3 publications

References 35 publications

A Systematic Literature Review of Deep Learning Approaches for Sketch-Based Image Retrieval: Datasets, Metrics, and Future Directions

A Systematic Literature Review of Deep Learning Approaches for Sketch-Based Image Retrieval: Datasets, Metrics, and Future Directions

Unsupervised contrastive learning: Shale porosity prediction based on conventional well logging

Cross Domain Visual Search with Feature Learning using Multi-stream Transformer-based Architectures

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